Index
Methodological Advances in Research on Social Movements, Conflict, and Change
ISBN: 978-1-80117-887-7, eISBN: 978-1-80117-886-0
ISSN: 0163-786X
Publication date: 12 July 2023
This content is currently only available as a PDF
Citation
(2023), "Index", Maher, T.V. and Schoon, E.W. (Ed.) Methodological Advances in Research on Social Movements, Conflict, and Change (Research in Social Movements, Conflicts and Change, Vol. 47), Emerald Publishing Limited, Leeds, pp. 269-277. https://doi.org/10.1108/S0163-786X20230000047012
Publisher
:Emerald Publishing Limited
Copyright © 2023 Thomas V. Maher and Eric W. Schoon. Published under exclusive licence by Emerald Publishing Limited
INDEX
Abstracting, 77
Academic presses, 162
Academic publishes, 162
Activist archives, 123
African American Vernacular English (AAVE), 103–104
Agence France-Presse (AFP), 251–253
Algorithmic black boxes, 77–78
Alliance. See Coalitions
American Association of University Women, 240
American Liberty League, 191
American Political Science Association (APSA), 161
American Schools of Oriental Research Syrian Heritage Initiative (ASOR-CHI), 84
American Sociological Association (ASA), 161–162
Analytical data, 78–79
Analytical metadata, 80–81
Animal Enterprise Terrorism Act (2006), 155
Animal rights, 188
Anti-abortion movements, 188
Anti-alcohol movements, 194–195
Anti-extradition Fact Check, 248
Anti-Extradition Law Amendment Bill (Anti-ELAB), 8, 246
event catalog, 250–251
protests in Hong Kong (2019), 241, 246–247
Anti-Mask Law, 247
Antinuclear groups, 240
Apple Daily Hong Kong, 251–253
Application programming interfaces (APIs), 107–108
Archival data, 95
Archival research and epistemology, 94–96
Archives, 94–95
voices in, 134–138
Armed Conflict Location and Event Data (ACLED), 221
Art festivals, 17
Artificial intelligence (AI), 104
Automated event coding, 249–250
Automated event databases, 22–23
Belmont Principle of Respect for Persons, The, 155
Belmont principles, 6, 146–147
Belmont Report, The, 146
Biases of news sources, understanding potential limitations and, 40–42
Big data projects, 50
Black, Indigenous, and people of color (BIPOC), 149
Black feminist scholars, 171
Black rights movement, 205–206
Boolean operators, 43
Broken windows theory, 218
Channel News Asia (CNA), 251–253
Chinese University of Hong Kong (CUHK), 255
Civil Human Rights Front, 246–247
Clustering analysis, 26
Co-appearance, 254
Co-organizers, 254
Coalitional relationships, 255
Coalitions, 240
building coalition as strategy, 241–242
data and methods, 247–255
formation and collapse of coalitions in response to threats, 258–260
formation and dissolution in response to threats, 255–260
measuring event coalition, 254–255
threats and, 242–245
Coercive repression, 101–102
Cognitive process, 243
Collection, innovations in, 4–5
Collective action events, 19–20
Communal projects, 160
Communist movements, 188
Community events, 128
Community mobilization, 26, 29
Computational methods, 32–33
Computational syntactic methods, 241
Computational text analysis
contentious events and social movement research, 16–20
data, 21–22
environmental movement, 20–21
issues, 24–25, 28–29
methods, 22–26
newspapers as data, 18–19
organizational type, 25–26, 29
and potential for reducing bias in collecting data on social movements, 19–20
protests and events, 22–23, 26–27
results, 26–29
tactics, 24, 27–28
Computer-automated big data projects, 50
Confidence intervals, 222–225
Congressional investigations, 196, 198
Conservative groups, 191
Constituent data, 78–79
Constituent metadata, 79–80
Contentious events, 14, 16, 19–20
Contentious politics, 2
Count models, 220–221
fit appropriate count models and get predicted counts for risk factor, 222
Counter movements, 102
Coverage, 195–197
Criminological theory, 218
Crisp-set QCA, 196–197
Crowd Counting Consortium and Count Love, The, 20
Cultural contexts, 151–152
“Cultural Violence and Civilian Deaths” project, 73
Cumulative diffusion, 216
Data
data-driven clustering of topics, 25
data-mining platforms, 39
and modeling strategies, 225–226
newspapers as, 18–19
Data collection, 107–109
bias, 41
strategies, 59–61
Data Documentation Initiative (DDI), 73
Data quality, 74–75
provenance and data quality in satellite images, 73–76
Data sources, 3
aggregate as sources as possible, 50–52
hegemon, 52–53
selection for world-historical analysis, 49–53
Databases, 39–40
Deep-learning algorithms, 250
Delta method, 220–221, 223
Description bias, 41–42, 48
Deviant combinations and advantages of wide comparisons, 199–207
Diffusion, 217
process, 219
studies, 217–218
Digital data sources, 5
Digitized historical newspaper, 39–40
Digitized news sources, 65
use of, 42–43
Directorate-General of Antiquities and Museums in Syria (DGAM in Syria), 81–82
Disruptiveness, 195
Dissidents, 244
Domestic news, dealing with, 54–56
Dove satellites, 75–76
Dramatic ramifications, 137–138
Dummy variable format, 57
Dynamics of Collective Action (DoCA), 217
articles, 23
data, 221, 226
dataset, 226–227
project, 206
Earth First! (organization), 20–21
EBSCO Regional Business News database, 21
Emotional “twinges”, 136–137
Emotional danger, 149
Emotional exploration, 137
Emotional risk, 152–153
Emotions and interactions, 134–138
Empirical analysis, 241
Enacted policy, 195–196
Encyclopedia of Associations, 21
Enforced policy, 195–196
Environmental movement, 20–21, 200
Environmental movement organizations (EMOs), 15, 21
Environmental sciences, 71
Ethical standards for student participation in protest research
Belmont principles, 146–147
ethics in practice, 147–158
Ethics, 71
in practice, 147–158
Ethnographic research, 179
Event coalition, 256–257
measuring, 254–255
Event coding, 241
Event diffusion momentum (EDM), 7, 216, 220, 233
calculate EDM and confidence intervals, 222–225
count number of pre/post-event protests, 222
diffusion studies, 217–218
explanations of protest diffusion, 218–219
fit appropriate count models and get predicted counts for each risk factor, 222
identifying risk factors of protest diffusion, 226–230
macro-micro analysis of effects of governors’ party affiliations and policing, 232–233
proposed method for estimating EDM, 220–225
research design, 225–226
select event-based data, 221
set temporal and spatial ranges of interest, 221–222
toward inter-event approach, 219–220
US presidents’ and governors’ party affiliations and diffusion, 230–231
Event history analysis (EHA), 219
Event-based data, select, 221
Events, protests and, 22–23
Expansionary forms of nationalism, 39
Facebook, 98
Faculty research projects, 144
False negatives, strategies to reduce, 45–46
Feminist archivists, 125
Feminist ethnography
boundaries and silences, 127–134
methods, 6
origin of social movement archives, 122–127
voices in archives, 134–138
Feminist organizations, 126–127
Feminist research, 122
Field research, 170
insider-ness and outsider-ness in, 170–172
Gale, 52
Gale Cengage Learning, 21, 39, 43
Gay liberation movement, 126
Gentle repression, 244
Geographic information systems (GIS), 78–79
Geographic sciences, 71
Geographical selection bias, 57
checking for effect of repeated mentions, 57
dealing with domestic news, 54–56
minimizing, 53–57
tracing geographical sensitivities, 56
Geographical sensitivities, tracing, 56
Global Data on Events Language and Tone (GDELT), 19–20, 22–23, 38–39, 50
Graduate Assistants, 173
Graduate Student Union Campaign, 173, 177–178, 180–181
Graduate students engaged in protest research, ethical treatment of, 147–158
Graduate training, 144–145
programs, 147
Green State University (GSU), 173
Greenpeace (confrontational EMO), 18–21, 200
Ground truthing
process, 83, 85
techniques, 71
Guardian/The Observer (G/O), 39
High–resolution satellite images, 69–71
Historical connection, 240
Historical newspaper, 43
Historical sources, 59–61
Hong Kong Protests (2019), 228
Indiscriminate repression, 245
Indiscriminate threats, 241, 244–245
Inmediahk. net, 251–253
Innovations, 216
in collection and processing, 4–5
epistemology and reflexivity, 5–7
novel analytics, 7–8
Insider-ness in field research, 170–172
Insider–outsider dynamics and identity in qualitative studies of social movements
case studies, 172–173
graduate student union campaign, 173
insider-ness and outsider-ness in field research, 170–172
Latinx Millennial Student organizing, 172–173
level of participation within movements, 175–179
questions for future researchers, 175, 179, 181
Ragon, 174–175
recognizing and negotiating interplay of researcher and participant identities, 174–175
Reyes, 174–175
securing research approval within institutional settings, 179–181
Instagram, 98–99, 111
Instant archives, 94, 96, 98
archival research and epistemology, 94–96
data and data collection, 105–111
data collection, 107–109
instant archive, 96–98
limitations of single archives and importance of triangulation and empirical approaches, 110–111
methodological decisions, 105–106
pace or life cycle of platform/archive, 106
platform design and evolution, 98–101
repression in, 101–103
research question about individuals, communities, organizations, or discourse, 105–106
sampling and sample size, 109–110
understanding data, 106–107
user innovations, 103–104
Institution-based research on social movements, 171–172
Institutional change, 26, 29
Institutional context, 240
Institutional identity, 173–175
Institutional mediation model of social movement news coverage, 189–193
Institutional repression, 244
Institutional Review Board (IRB), 145, 179, 181
Institutional settings, securing research approval within, 179–181
Institutions, 216
“Insurgent Artifacts” project, 72–73, 83–84
Insurgent groups, 72, 245
Integrated Crisis Early Warning System (ICEWS), 38–39
Inter-event approach, 7, 219–220
Inter-organizational coalitions, 240
“Inter-protest” dynamics, 219–220
Interactions, emotions and, 134–138
International Crisis Early Warning System (ICEWS), 19–20, 22–23
Irredentism, 39
Journals, 162
Justice, 157–158
Karatasli’s analysis, 63–64
Keyword strings, 43, 45
working with, 43–49
Land Remote Sensing Policy Act (1992) (LRSPA), 75
Latinx Millennial Student, 172–173, 176–177, 180
Latinx student organizations, 172–173
League of Women Voters, 240
Legal risks, 153–155
Lesbian feminists, 139
LexisNexis (Online platforms), 39, 43, 52
Liberal Arts College (LAC), 172–173
Log link function, 223–225
Logical remainders, 203–204
Looters’ pits, 78, 81
Looting, 72
LRT–Vuong model selection, 226
Machine algorithms, 73
Machine learning, 19–20, 249
machine learning-based big data projects, 50
machine-learning-based event coding projects, 250
techniques, 15
Machine-learning Protest Event Data System (MPEDS), 22–23
Macro-micro analysis of effects of governors’ party affiliations and policing, 232–233
results, 232–233
variables, 232
Manual data collection, 107–108
Marginalized communities, 124–125, 129–130, 139
Marginalized social movement communities, 122–123
Metadata, 78–79
incorporating metadata and corroborating satellite images, 81–86
Michigan Womyn’s Music Festival, 126
Microsatellites, 75–76
Ming Po, 251–253
Mobilization processes, 220
Music, 17
festivals, 126
National chauvinism, 39
National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, The, 146
National independence wars, 63
National newspapers, 18–19
National Organization for Women (NOW), 126–127, 240
National Research Act (1974), 146
National Science Foundation’s funds for emergent research, 161–162
National US social movement organizations, 208
National Women’s Music Festival, 126
Nationalism, 38
datasets of, 38
scholars of, 38–39
Nationalist mobilization, 38
Nationalist movements, 39
Natural language processing technologies (NLP technologies), 15, 19–20, 38–39, 47
New Left movements, 16
New York Times (NYT), 15, 39
News coverage, 188
News media, 190, 209–210
Newspaper
boundaries of true and false positives, 47–49
changing reporting styles of newspapers, 46–47
data, 14–15, 18–19
using newspaper archives for collecting macro-historical data, 40–43
understanding potential limitations and biases of news sources, 40–42
use of digitized news sources, 42–43
Nexis Uni newspaper database, 21–23
North Eastern Women’s Music Retreat (NEWMR), 136–137
planning documents, 137–138
Novel analytics, 7–8
Objectivity in satellite images, algorithms and illusion of, 76–78
Olson method, 77
Online ethnographies, 110
Online platforms, 43
Operationalization, 106–107
Organizational strength, 195
Organizational structure, 240
Organizational type, 25–26
Organizing Together (OT), 181
Outsider-ness in field research, 170–172
Palestinian nationalism, 41
Participant identities, recognizing and negotiating interplay of, 174–175
Participation within movements, determining level of, 175–179
Partisan political context, 195–196
PATRIOT Act (2001), 155
People for the Ethical Treatment of Animals (PETA), 20–21
Periodization, 257–258
Personal transformation, 26
Planet (private commercial satellite company), 75–76
Platform design and evolution, 98–101
Platform/archive, pace or life cycle of, 106
Policing, 232–233
Political mediation models, 192
Political opportunity theory, 230
Political Organization in the News (PONs), 189
Political right, 195
Political sociology, 207–208
Post-event protests, count number of, 222
Pre/post-event protests, count number of, 222
Pro-democracy activists, 243
Processing, innovations in, 4–5
Professional associations, 161–162
Professional organizations, 160–162
sections of, 162
Professional risks, 153
ProQuest (Online platforms), 39, 43, 52
Protest diffusion, 215–216
explanations of, 218–219
forms of action, 227
identifying risk factors of, 226–230
method, 228
results, 228–230
size of protest, 227
social movement organizations, 227–228
studies, 219
variables and operationalization, 227–228
violence, 228
Protest research
beneficence, 148–155
benefits, 148–149
emotional risk, 152–153
ethical treatment of graduate students engaged in, 147–158
individual researchers, 158
journals and academic presses, 162
justice, 157–158
legal risks, 153–155
physical risks, 150–152
practice, 158–163
professional organizations, 160–162
professional risks, 153
respect for persons, 155–157
risks, 149–150
sections of professional organizations, 162
throughout profession, 162–163
Protests, 16, 19–20
for Black Lives, 144
events, 18, 22–23, 26–27, 31, 255
Provenance, 74
concept of, 74
Public education programs, 14
Public health and social science perspectives, 217–218
QAnon, 110–111
Qualitative comparative analysis (QCA), 7, 189, 193–194
data, methods, and measures, 193–196
four hypotheses and QCA results, 196–199
institutional mediation model of social movement news coverage, 189–193
truth tables, Venn diagrams, deviant combinations, and advantages of wide comparisons, 199–207
Qualitative methods course, 177–178
Reagan conservative regime, 200–202
Recognition, 2–3
Regional Public University (RPU), 172–173, 176–177
Repression, 243–244
in instant archive, 101–103
Republican conservative regime, 200–202
Research, 145
participants, 146
process, 122, 148
securing research approval within institutional settings, 179–181
Research design, 225–226
DoCA data, 226
zero-inflated poisson models, 226
Research in Social Movements, Conflict & Change (RSMCC), 1
Research University (RU), 172–173, 176
Researcher, recognizing and negotiating interplay of, 174–175
Researcher bias, 45–47
Resolution, 75
Resource mobilization theory, 188, 233–234
Resources, 240
Risk assessment, 159
Rule-based programming, 249
Rule-based text analysis, 241
Russian military, 69–70
Safety, 144
Satellite imagery, 3
Satellite images, 70, 86
algorithms and illusion of objectivity in, 76–78
case studies and methods, 71–73
incorporating metadata and corroborating satellite images, 81–86
provenance and data quality in satellite images, 73–76
responsible practices for satellite research, 86–88
satellite metadata, 78–81
Satellite sensors, 77
Scale shift, 219–220
Science and technology studies (STS), 73
Search strategy of SSNM dataset, 46
Selection bias, 40–41
Selective repression, 245
Selective threats, 241, 244–245
Severe repression, 244
Shared identity, 240, 242
Shared ideology, 242
Smith College library website, The, 123–124
Social, Political, and Economic Event Database (SPEED), 19–20
Social identities, 169–171, 174
Social media, 3, 93–94, 111–112, 241
companies, 103
data, 94, 108–110
mobilization, 93–94
platforms, 98, 100, 102
Social movement organizations (SMOs), 18–19, 215–216, 227–228
Social movements, 14, 17, 31, 38, 41–42, 144–145, 158, 161–162, 171–172, 188, 227, 240
computational text analysis and potential for reducing bias in collecting data on, 19–20
institutional mediation model of social movement news coverage, 189–193
origin of social movement archives, 122–127
research, 16–20
researchers, 94, 105, 107, 153, 219, 230
scholars, 14–18, 144
South China Morning Post, 251–253
SPEED, 22–23
Standpoint concept, 171
State Actions, 255
State repression, 243–244
State-induced threats, 240
State-led nationalism, 39
State-seeking nationalism, 39
State-Seeking Nationalist Movements (SSNM), 4, 61–62
controlling for increasing number of pages over time, 57–58
data source selection for world-historical analysis, 49–53
dataset I, 43
dataset II, 39
minimizing geographical selection bias, 53–57
using newspaper archives for collecting macro-historical data, 40–43
reliability of SSNM dataset I, 58–64
strategies to reduce false negatives, 45–46
working with keyword strings, 43–49
State-seeking nationalist movements, 39
Strategies, 20
Structural topic modeling (STM), 24–25
Supervised machine-learning methods, 249–250
Syntactic rule-based methods, 8
Syrian civil war, 86–87
Tactics, 20, 24, 27–28
Telegram, 8, 241
comparing events extracted from Telegram and Newspaper sources, 251–253
Telegram Broadcasting Posts, 247–249
Threats, 242–245
Threshold effect, 40–41, 48–49
TikTok, 98–99, 111
Topic models, 24–25
Toronto G20 Summit Protests (2010), 228
Trans-exclusionary radical feminists (TERFs), 132–133
Triangulation and empirical approaches, limitations of single archives and importance of, 110–111
Truth tables and advantages of wide comparisons, 199–207
Twitter, 98
United Kingdom (UK), 39
UK-based newspapers, 56
United States (US), 39
method, 230–231
party affiliation, 230
presidents’ and governors’ party affiliations and diffusion, 230–231
results, 231
US social movements, 193–194, 206
US-based newspapers, 56
variables, 230
Unmanned Aero Systems (UAS), 74
Uppsala Conflict Data Program (UCDP), 73
UCDP/PRIO Armed Conflict Dataset, 221
Venn diagrams and advantages of wide comparisons, 199–207
Veterans, 194–195
Violence, 228
Web scraping, 107–108
Web-crawling techniques, 20
Wen Wei Po, 251–253
White supremacists, 188, 191
Women’s Music Archives, 6, 124–125, 135
case of, 125
World Labor Group database (WLG database), 52
World Wildlife Fund (nonconfrontational EMO), 18–19
YouTube, 111
Yusin Constitution, 243
Zero-Inflated Poisson Models (ZIP models), 226
Abstracting
, 77
Academic presses
, 162
Academic publishes
, 162
Activist archives
, 123
African American Vernacular English (AAVE)
, 103–104
Agence France-Presse (AFP)
, 251–253
Algorithmic black boxes
, 77–78
Alliance. See Coalitions
American Association of University Women
, 240
American Liberty League
, 191
American Political Science Association (APSA)
, 161
American Schools of Oriental Research Syrian Heritage Initiative (ASOR-CHI)
, 84
American Sociological Association (ASA)
, 161–162
Analytical data
, 78–79
Analytical metadata
, 80–81
Animal Enterprise Terrorism Act (2006)
, 155
Animal rights
, 188
Anti-abortion movements
, 188
Anti-alcohol movements
, 194–195
Anti-extradition Fact Check
, 248
Anti-Extradition Law Amendment Bill (Anti-ELAB)
, 8, 246
event catalog
, 250–251
protests in Hong Kong (2019)
, 241, 246–247
Anti-Mask Law
, 247
Antinuclear groups
, 240
Apple Daily Hong Kong
, 251–253
Application programming interfaces (APIs)
, 107–108
Archival data
, 95
Archival research and epistemology
, 94–96
Archives
, 94–95
voices in
, 134–138
Armed Conflict Location and Event Data (ACLED)
, 221
Art festivals
, 17
Artificial intelligence (AI)
, 104
Automated event coding
, 249–250
Automated event databases
, 22–23
Belmont Principle of Respect for Persons, The
, 155
Belmont principles
, 6, 146–147
Belmont Report, The
, 146
Biases of news sources, understanding potential limitations and
, 40–42
Big data projects
, 50
Black, Indigenous, and people of color (BIPOC)
, 149
Black feminist scholars
, 171
Black rights movement
, 205–206
Boolean operators
, 43
Broken windows theory
, 218
Channel News Asia (CNA), 251–253
Chinese University of Hong Kong (CUHK), 255
Civil Human Rights Front, 246–247
Clustering analysis, 26
Co-appearance, 254
Co-organizers, 254
Coalitional relationships, 255
Coalitions, 240
building coalition as strategy, 241–242
data and methods, 247–255
formation and collapse of coalitions in response to threats, 258–260
formation and dissolution in response to threats, 255–260
measuring event coalition, 254–255
threats and, 242–245
Coercive repression, 101–102
Cognitive process, 243
Collection, innovations in, 4–5
Collective action events, 19–20
Communal projects, 160
Communist movements, 188
Community events, 128
Community mobilization, 26, 29
Computational methods, 32–33
Computational syntactic methods, 241
Computational text analysis
contentious events and social movement research, 16–20
data, 21–22
environmental movement, 20–21
issues, 24–25, 28–29
methods, 22–26
newspapers as data, 18–19
organizational type, 25–26, 29
and potential for reducing bias in collecting data on social movements, 19–20
protests and events, 22–23, 26–27
results, 26–29
tactics, 24, 27–28
Computer-automated big data projects, 50
Confidence intervals, 222–225
Congressional investigations, 196, 198
Conservative groups, 191
Constituent data, 78–79
Constituent metadata, 79–80
Contentious events, 14, 16, 19–20
Contentious politics, 2
Count models, 220–221
fit appropriate count models and get predicted counts for risk factor, 222
Counter movements, 102
Coverage, 195–197
Criminological theory, 218
Crisp-set QCA, 196–197
Crowd Counting Consortium and Count Love, The, 20
Cultural contexts, 151–152
“Cultural Violence and Civilian Deaths” project, 73
Cumulative diffusion, 216
Data
data-driven clustering of topics, 25
data-mining platforms, 39
and modeling strategies, 225–226
newspapers as, 18–19
Data collection, 107–109
bias, 41
strategies, 59–61
Data Documentation Initiative (DDI), 73
Data quality, 74–75
provenance and data quality in satellite images, 73–76
Data sources, 3
aggregate as sources as possible, 50–52
hegemon, 52–53
selection for world-historical analysis, 49–53
Databases, 39–40
Deep-learning algorithms, 250
Delta method, 220–221, 223
Description bias, 41–42, 48
Deviant combinations and advantages of wide comparisons, 199–207
Diffusion, 217
process, 219
studies, 217–218
Digital data sources, 5
Digitized historical newspaper, 39–40
Digitized news sources, 65
use of, 42–43
Directorate-General of Antiquities and Museums in Syria (DGAM in Syria), 81–82
Disruptiveness, 195
Dissidents, 244
Domestic news, dealing with, 54–56
Dove satellites, 75–76
Dramatic ramifications, 137–138
Dummy variable format, 57
Dynamics of Collective Action (DoCA), 217
articles, 23
data, 221, 226
dataset, 226–227
project, 206
Earth First! (organization), 20–21
EBSCO Regional Business News database, 21
Emotional “twinges”, 136–137
Emotional danger, 149
Emotional exploration, 137
Emotional risk, 152–153
Emotions and interactions, 134–138
Empirical analysis, 241
Enacted policy, 195–196
Encyclopedia of Associations, 21
Enforced policy, 195–196
Environmental movement, 20–21, 200
Environmental movement organizations (EMOs), 15, 21
Environmental sciences, 71
Ethical standards for student participation in protest research
Belmont principles, 146–147
ethics in practice, 147–158
Ethics, 71
in practice, 147–158
Ethnographic research, 179
Event coalition, 256–257
measuring, 254–255
Event coding, 241
Event diffusion momentum (EDM), 7, 216, 220, 233
calculate EDM and confidence intervals, 222–225
count number of pre/post-event protests, 222
diffusion studies, 217–218
explanations of protest diffusion, 218–219
fit appropriate count models and get predicted counts for each risk factor, 222
identifying risk factors of protest diffusion, 226–230
macro-micro analysis of effects of governors’ party affiliations and policing, 232–233
proposed method for estimating EDM, 220–225
research design, 225–226
select event-based data, 221
set temporal and spatial ranges of interest, 221–222
toward inter-event approach, 219–220
US presidents’ and governors’ party affiliations and diffusion, 230–231
Event history analysis (EHA), 219
Event-based data, select, 221
Events, protests and, 22–23
Expansionary forms of nationalism, 39
Facebook, 98
Faculty research projects, 144
False negatives, strategies to reduce, 45–46
Feminist archivists, 125
Feminist ethnography
boundaries and silences, 127–134
methods, 6
origin of social movement archives, 122–127
voices in archives, 134–138
Feminist organizations, 126–127
Feminist research, 122
Field research, 170
insider-ness and outsider-ness in, 170–172
Gale, 52
Gale Cengage Learning, 21, 39, 43
Gay liberation movement, 126
Gentle repression, 244
Geographic information systems (GIS), 78–79
Geographic sciences, 71
Geographical selection bias, 57
checking for effect of repeated mentions, 57
dealing with domestic news, 54–56
minimizing, 53–57
tracing geographical sensitivities, 56
Geographical sensitivities, tracing, 56
Global Data on Events Language and Tone (GDELT), 19–20, 22–23, 38–39, 50
Graduate Assistants, 173
Graduate Student Union Campaign, 173, 177–178, 180–181
Graduate students engaged in protest research, ethical treatment of, 147–158
Graduate training, 144–145
programs, 147
Green State University (GSU), 173
Greenpeace (confrontational EMO), 18–21, 200
Ground truthing
process, 83, 85
techniques, 71
Guardian/The Observer (G/O), 39
High–resolution satellite images, 69–71
Historical connection, 240
Historical newspaper, 43
Historical sources, 59–61
Hong Kong Protests (2019), 228
Indiscriminate repression, 245
Indiscriminate threats, 241, 244–245
Inmediahk. net, 251–253
Innovations, 216
in collection and processing, 4–5
epistemology and reflexivity, 5–7
novel analytics, 7–8
Insider-ness in field research, 170–172
Insider–outsider dynamics and identity in qualitative studies of social movements
case studies, 172–173
graduate student union campaign, 173
insider-ness and outsider-ness in field research, 170–172
Latinx Millennial Student organizing, 172–173
level of participation within movements, 175–179
questions for future researchers, 175, 179, 181
Ragon, 174–175
recognizing and negotiating interplay of researcher and participant identities, 174–175
Reyes, 174–175
securing research approval within institutional settings, 179–181
Instagram, 98–99, 111
Instant archives, 94, 96, 98
archival research and epistemology, 94–96
data and data collection, 105–111
data collection, 107–109
instant archive, 96–98
limitations of single archives and importance of triangulation and empirical approaches, 110–111
methodological decisions, 105–106
pace or life cycle of platform/archive, 106
platform design and evolution, 98–101
repression in, 101–103
research question about individuals, communities, organizations, or discourse, 105–106
sampling and sample size, 109–110
understanding data, 106–107
user innovations, 103–104
Institution-based research on social movements, 171–172
Institutional change, 26, 29
Institutional context, 240
Institutional identity, 173–175
Institutional mediation model of social movement news coverage, 189–193
Institutional repression, 244
Institutional Review Board (IRB), 145, 179, 181
Institutional settings, securing research approval within, 179–181
Institutions, 216
“Insurgent Artifacts” project, 72–73, 83–84
Insurgent groups, 72, 245
Integrated Crisis Early Warning System (ICEWS), 38–39
Inter-event approach, 7, 219–220
Inter-organizational coalitions, 240
“Inter-protest” dynamics, 219–220
Interactions, emotions and, 134–138
International Crisis Early Warning System (ICEWS), 19–20, 22–23
Irredentism, 39
Journals, 162
Justice, 157–158
Karatasli’s analysis, 63–64
Keyword strings, 43, 45
working with, 43–49
Land Remote Sensing Policy Act (1992) (LRSPA), 75
Latinx Millennial Student, 172–173, 176–177, 180
Latinx student organizations, 172–173
League of Women Voters, 240
Legal risks, 153–155
Lesbian feminists, 139
LexisNexis (Online platforms), 39, 43, 52
Liberal Arts College (LAC), 172–173
Log link function, 223–225
Logical remainders, 203–204
Looters’ pits, 78, 81
Looting, 72
LRT–Vuong model selection, 226
Machine algorithms, 73
Machine learning, 19–20, 249
machine learning-based big data projects, 50
machine-learning-based event coding projects, 250
techniques, 15
Machine-learning Protest Event Data System (MPEDS), 22–23
Macro-micro analysis of effects of governors’ party affiliations and policing, 232–233
results, 232–233
variables, 232
Manual data collection, 107–108
Marginalized communities, 124–125, 129–130, 139
Marginalized social movement communities, 122–123
Metadata, 78–79
incorporating metadata and corroborating satellite images, 81–86
Michigan Womyn’s Music Festival, 126
Microsatellites, 75–76
Ming Po, 251–253
Mobilization processes, 220
Music, 17
festivals, 126
National chauvinism, 39
National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, The, 146
National independence wars, 63
National newspapers, 18–19
National Organization for Women (NOW), 126–127, 240
National Research Act (1974), 146
National Science Foundation’s funds for emergent research, 161–162
National US social movement organizations, 208
National Women’s Music Festival, 126
Nationalism, 38
datasets of, 38
scholars of, 38–39
Nationalist mobilization, 38
Nationalist movements, 39
Natural language processing technologies (NLP technologies), 15, 19–20, 38–39, 47
New Left movements, 16
New York Times (NYT), 15, 39
News coverage, 188
News media, 190, 209–210
Newspaper
boundaries of true and false positives, 47–49
changing reporting styles of newspapers, 46–47
data, 14–15, 18–19
using newspaper archives for collecting macro-historical data, 40–43
understanding potential limitations and biases of news sources, 40–42
use of digitized news sources, 42–43
Nexis Uni newspaper database, 21–23
North Eastern Women’s Music Retreat (NEWMR), 136–137
planning documents, 137–138
Novel analytics, 7–8
Objectivity in satellite images, algorithms and illusion of, 76–78
Olson method, 77
Online ethnographies, 110
Online platforms, 43
Operationalization, 106–107
Organizational strength, 195
Organizational structure, 240
Organizational type, 25–26
Organizing Together (OT), 181
Outsider-ness in field research, 170–172
Palestinian nationalism, 41
Participant identities, recognizing and negotiating interplay of, 174–175
Participation within movements, determining level of, 175–179
Partisan political context, 195–196
PATRIOT Act (2001), 155
People for the Ethical Treatment of Animals (PETA), 20–21
Periodization, 257–258
Personal transformation, 26
Planet (private commercial satellite company), 75–76
Platform design and evolution, 98–101
Platform/archive, pace or life cycle of, 106
Policing, 232–233
Political mediation models, 192
Political opportunity theory, 230
Political Organization in the News (PONs), 189
Political right, 195
Political sociology, 207–208
Post-event protests, count number of, 222
Pre/post-event protests, count number of, 222
Pro-democracy activists, 243
Processing, innovations in, 4–5
Professional associations, 161–162
Professional organizations, 160–162
sections of, 162
Professional risks, 153
ProQuest (Online platforms), 39, 43, 52
Protest diffusion, 215–216
explanations of, 218–219
forms of action, 227
identifying risk factors of, 226–230
method, 228
results, 228–230
size of protest, 227
social movement organizations, 227–228
studies, 219
variables and operationalization, 227–228
violence, 228
Protest research
beneficence, 148–155
benefits, 148–149
emotional risk, 152–153
ethical treatment of graduate students engaged in, 147–158
individual researchers, 158
journals and academic presses, 162
justice, 157–158
legal risks, 153–155
physical risks, 150–152
practice, 158–163
professional organizations, 160–162
professional risks, 153
respect for persons, 155–157
risks, 149–150
sections of professional organizations, 162
throughout profession, 162–163
Protests, 16, 19–20
for Black Lives, 144
events, 18, 22–23, 26–27, 31, 255
Provenance, 74
concept of, 74
Public education programs, 14
Public health and social science perspectives, 217–218
QAnon, 110–111
Qualitative comparative analysis (QCA), 7, 189, 193–194
data, methods, and measures, 193–196
four hypotheses and QCA results, 196–199
institutional mediation model of social movement news coverage, 189–193
truth tables, Venn diagrams, deviant combinations, and advantages of wide comparisons, 199–207
Qualitative methods course, 177–178
Reagan conservative regime, 200–202
Recognition, 2–3
Regional Public University (RPU), 172–173, 176–177
Repression, 243–244
in instant archive, 101–103
Republican conservative regime, 200–202
Research, 145
participants, 146
process, 122, 148
securing research approval within institutional settings, 179–181
Research design, 225–226
DoCA data, 226
zero-inflated poisson models, 226
Research in Social Movements, Conflict & Change (RSMCC), 1
Research University (RU), 172–173, 176
Researcher, recognizing and negotiating interplay of, 174–175
Researcher bias, 45–47
Resolution, 75
Resource mobilization theory, 188, 233–234
Resources, 240
Risk assessment, 159
Rule-based programming, 249
Rule-based text analysis, 241
Russian military, 69–70
Safety, 144
Satellite imagery, 3
Satellite images, 70, 86
algorithms and illusion of objectivity in, 76–78
case studies and methods, 71–73
incorporating metadata and corroborating satellite images, 81–86
provenance and data quality in satellite images, 73–76
responsible practices for satellite research, 86–88
satellite metadata, 78–81
Satellite sensors, 77
Scale shift, 219–220
Science and technology studies (STS), 73
Search strategy of SSNM dataset, 46
Selection bias, 40–41
Selective repression, 245
Selective threats, 241, 244–245
Severe repression, 244
Shared identity, 240, 242
Shared ideology, 242
Smith College library website, The, 123–124
Social, Political, and Economic Event Database (SPEED), 19–20
Social identities, 169–171, 174
Social media, 3, 93–94, 111–112, 241
companies, 103
data, 94, 108–110
mobilization, 93–94
platforms, 98, 100, 102
Social movement organizations (SMOs), 18–19, 215–216, 227–228
Social movements, 14, 17, 31, 38, 41–42, 144–145, 158, 161–162, 171–172, 188, 227, 240
computational text analysis and potential for reducing bias in collecting data on, 19–20
institutional mediation model of social movement news coverage, 189–193
origin of social movement archives, 122–127
research, 16–20
researchers, 94, 105, 107, 153, 219, 230
scholars, 14–18, 144
South China Morning Post, 251–253
SPEED, 22–23
Standpoint concept, 171
State Actions, 255
State repression, 243–244
State-induced threats, 240
State-led nationalism, 39
State-seeking nationalism, 39
State-Seeking Nationalist Movements (SSNM), 4, 61–62
controlling for increasing number of pages over time, 57–58
data source selection for world-historical analysis, 49–53
dataset I, 43
dataset II, 39
minimizing geographical selection bias, 53–57
using newspaper archives for collecting macro-historical data, 40–43
reliability of SSNM dataset I, 58–64
strategies to reduce false negatives, 45–46
working with keyword strings, 43–49
State-seeking nationalist movements, 39
Strategies, 20
Structural topic modeling (STM), 24–25
Supervised machine-learning methods, 249–250
Syntactic rule-based methods, 8
Syrian civil war, 86–87
Tactics, 20, 24, 27–28
Telegram, 8, 241
comparing events extracted from Telegram and Newspaper sources, 251–253
Telegram Broadcasting Posts, 247–249
Threats, 242–245
Threshold effect, 40–41, 48–49
TikTok, 98–99, 111
Topic models, 24–25
Toronto G20 Summit Protests (2010), 228
Trans-exclusionary radical feminists (TERFs), 132–133
Triangulation and empirical approaches, limitations of single archives and importance of, 110–111
Truth tables and advantages of wide comparisons, 199–207
Twitter, 98
United Kingdom (UK), 39
UK-based newspapers, 56
United States (US), 39
method, 230–231
party affiliation, 230
presidents’ and governors’ party affiliations and diffusion, 230–231
results, 231
US social movements, 193–194, 206
US-based newspapers, 56
variables, 230
Unmanned Aero Systems (UAS), 74
Uppsala Conflict Data Program (UCDP), 73
UCDP/PRIO Armed Conflict Dataset, 221
Venn diagrams and advantages of wide comparisons, 199–207
Veterans, 194–195
Violence, 228
Web scraping, 107–108
Web-crawling techniques, 20
Wen Wei Po, 251–253
White supremacists, 188, 191
Women’s Music Archives, 6, 124–125, 135
case of, 125
World Labor Group database (WLG database), 52
World Wildlife Fund (nonconfrontational EMO), 18–19
YouTube, 111
Yusin Constitution, 243
Zero-Inflated Poisson Models (ZIP models), 226
Channel News Asia (CNA)
, 251–253
Chinese University of Hong Kong (CUHK)
, 255
Civil Human Rights Front
, 246–247
Clustering analysis
, 26
Co-appearance
, 254
Co-organizers
, 254
Coalitional relationships
, 255
Coalitions
, 240
building coalition as strategy
, 241–242
data and methods
, 247–255
formation and collapse of coalitions in response to threats
, 258–260
formation and dissolution in response to threats
, 255–260
measuring event coalition
, 254–255
threats and
, 242–245
Coercive repression
, 101–102
Cognitive process
, 243
Collection, innovations in
, 4–5
Collective action events
, 19–20
Communal projects
, 160
Communist movements
, 188
Community events
, 128
Community mobilization
, 26, 29
Computational methods
, 32–33
Computational syntactic methods
, 241
Computational text analysis
contentious events and social movement research
, 16–20
data
, 21–22
environmental movement
, 20–21
issues
, 24–25, 28–29
methods
, 22–26
newspapers as data
, 18–19
organizational type
, 25–26, 29
and potential for reducing bias in collecting data on social movements
, 19–20
protests and events
, 22–23, 26–27
results
, 26–29
tactics
, 24, 27–28
Computer-automated big data projects
, 50
Confidence intervals
, 222–225
Congressional investigations
, 196, 198
Conservative groups
, 191
Constituent data
, 78–79
Constituent metadata
, 79–80
Contentious events
, 14, 16, 19–20
Contentious politics
, 2
Count models
, 220–221
fit appropriate count models and get predicted counts for risk factor
, 222
Counter movements
, 102
Coverage
, 195–197
Criminological theory
, 218
Crisp-set QCA
, 196–197
Crowd Counting Consortium and Count Love, The
, 20
Cultural contexts
, 151–152
“Cultural Violence and Civilian Deaths” project
, 73
Cumulative diffusion
, 216
Data
data-driven clustering of topics
, 25
data-mining platforms
, 39
and modeling strategies
, 225–226
newspapers as
, 18–19
Data collection
, 107–109
bias
, 41
strategies
, 59–61
Data Documentation Initiative (DDI)
, 73
Data quality
, 74–75
provenance and data quality in satellite images
, 73–76
Data sources
, 3
aggregate as sources as possible
, 50–52
hegemon
, 52–53
selection for world-historical analysis
, 49–53
Databases
, 39–40
Deep-learning algorithms
, 250
Delta method
, 220–221, 223
Description bias
, 41–42, 48
Deviant combinations and advantages of wide comparisons
, 199–207
Diffusion
, 217
process
, 219
studies
, 217–218
Digital data sources
, 5
Digitized historical newspaper
, 39–40
Digitized news sources
, 65
use of
, 42–43
Directorate-General of Antiquities and Museums in Syria (DGAM in Syria)
, 81–82
Disruptiveness
, 195
Dissidents
, 244
Domestic news, dealing with
, 54–56
Dove satellites
, 75–76
Dramatic ramifications
, 137–138
Dummy variable format
, 57
Dynamics of Collective Action (DoCA)
, 217
articles
, 23
data
, 221, 226
dataset
, 226–227
project
, 206
Earth First! (organization), 20–21
EBSCO Regional Business News database, 21
Emotional “twinges”, 136–137
Emotional danger, 149
Emotional exploration, 137
Emotional risk, 152–153
Emotions and interactions, 134–138
Empirical analysis, 241
Enacted policy, 195–196
Encyclopedia of Associations, 21
Enforced policy, 195–196
Environmental movement, 20–21, 200
Environmental movement organizations (EMOs), 15, 21
Environmental sciences, 71
Ethical standards for student participation in protest research
Belmont principles, 146–147
ethics in practice, 147–158
Ethics, 71
in practice, 147–158
Ethnographic research, 179
Event coalition, 256–257
measuring, 254–255
Event coding, 241
Event diffusion momentum (EDM), 7, 216, 220, 233
calculate EDM and confidence intervals, 222–225
count number of pre/post-event protests, 222
diffusion studies, 217–218
explanations of protest diffusion, 218–219
fit appropriate count models and get predicted counts for each risk factor, 222
identifying risk factors of protest diffusion, 226–230
macro-micro analysis of effects of governors’ party affiliations and policing, 232–233
proposed method for estimating EDM, 220–225
research design, 225–226
select event-based data, 221
set temporal and spatial ranges of interest, 221–222
toward inter-event approach, 219–220
US presidents’ and governors’ party affiliations and diffusion, 230–231
Event history analysis (EHA), 219
Event-based data, select, 221
Events, protests and, 22–23
Expansionary forms of nationalism, 39
Facebook, 98
Faculty research projects, 144
False negatives, strategies to reduce, 45–46
Feminist archivists, 125
Feminist ethnography
boundaries and silences, 127–134
methods, 6
origin of social movement archives, 122–127
voices in archives, 134–138
Feminist organizations, 126–127
Feminist research, 122
Field research, 170
insider-ness and outsider-ness in, 170–172
Gale, 52
Gale Cengage Learning, 21, 39, 43
Gay liberation movement, 126
Gentle repression, 244
Geographic information systems (GIS), 78–79
Geographic sciences, 71
Geographical selection bias, 57
checking for effect of repeated mentions, 57
dealing with domestic news, 54–56
minimizing, 53–57
tracing geographical sensitivities, 56
Geographical sensitivities, tracing, 56
Global Data on Events Language and Tone (GDELT), 19–20, 22–23, 38–39, 50
Graduate Assistants, 173
Graduate Student Union Campaign, 173, 177–178, 180–181
Graduate students engaged in protest research, ethical treatment of, 147–158
Graduate training, 144–145
programs, 147
Green State University (GSU), 173
Greenpeace (confrontational EMO), 18–21, 200
Ground truthing
process, 83, 85
techniques, 71
Guardian/The Observer (G/O), 39
High–resolution satellite images, 69–71
Historical connection, 240
Historical newspaper, 43
Historical sources, 59–61
Hong Kong Protests (2019), 228
Indiscriminate repression, 245
Indiscriminate threats, 241, 244–245
Inmediahk. net, 251–253
Innovations, 216
in collection and processing, 4–5
epistemology and reflexivity, 5–7
novel analytics, 7–8
Insider-ness in field research, 170–172
Insider–outsider dynamics and identity in qualitative studies of social movements
case studies, 172–173
graduate student union campaign, 173
insider-ness and outsider-ness in field research, 170–172
Latinx Millennial Student organizing, 172–173
level of participation within movements, 175–179
questions for future researchers, 175, 179, 181
Ragon, 174–175
recognizing and negotiating interplay of researcher and participant identities, 174–175
Reyes, 174–175
securing research approval within institutional settings, 179–181
Instagram, 98–99, 111
Instant archives, 94, 96, 98
archival research and epistemology, 94–96
data and data collection, 105–111
data collection, 107–109
instant archive, 96–98
limitations of single archives and importance of triangulation and empirical approaches, 110–111
methodological decisions, 105–106
pace or life cycle of platform/archive, 106
platform design and evolution, 98–101
repression in, 101–103
research question about individuals, communities, organizations, or discourse, 105–106
sampling and sample size, 109–110
understanding data, 106–107
user innovations, 103–104
Institution-based research on social movements, 171–172
Institutional change, 26, 29
Institutional context, 240
Institutional identity, 173–175
Institutional mediation model of social movement news coverage, 189–193
Institutional repression, 244
Institutional Review Board (IRB), 145, 179, 181
Institutional settings, securing research approval within, 179–181
Institutions, 216
“Insurgent Artifacts” project, 72–73, 83–84
Insurgent groups, 72, 245
Integrated Crisis Early Warning System (ICEWS), 38–39
Inter-event approach, 7, 219–220
Inter-organizational coalitions, 240
“Inter-protest” dynamics, 219–220
Interactions, emotions and, 134–138
International Crisis Early Warning System (ICEWS), 19–20, 22–23
Irredentism, 39
Journals, 162
Justice, 157–158
Karatasli’s analysis, 63–64
Keyword strings, 43, 45
working with, 43–49
Land Remote Sensing Policy Act (1992) (LRSPA), 75
Latinx Millennial Student, 172–173, 176–177, 180
Latinx student organizations, 172–173
League of Women Voters, 240
Legal risks, 153–155
Lesbian feminists, 139
LexisNexis (Online platforms), 39, 43, 52
Liberal Arts College (LAC), 172–173
Log link function, 223–225
Logical remainders, 203–204
Looters’ pits, 78, 81
Looting, 72
LRT–Vuong model selection, 226
Machine algorithms, 73
Machine learning, 19–20, 249
machine learning-based big data projects, 50
machine-learning-based event coding projects, 250
techniques, 15
Machine-learning Protest Event Data System (MPEDS), 22–23
Macro-micro analysis of effects of governors’ party affiliations and policing, 232–233
results, 232–233
variables, 232
Manual data collection, 107–108
Marginalized communities, 124–125, 129–130, 139
Marginalized social movement communities, 122–123
Metadata, 78–79
incorporating metadata and corroborating satellite images, 81–86
Michigan Womyn’s Music Festival, 126
Microsatellites, 75–76
Ming Po, 251–253
Mobilization processes, 220
Music, 17
festivals, 126
National chauvinism, 39
National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, The, 146
National independence wars, 63
National newspapers, 18–19
National Organization for Women (NOW), 126–127, 240
National Research Act (1974), 146
National Science Foundation’s funds for emergent research, 161–162
National US social movement organizations, 208
National Women’s Music Festival, 126
Nationalism, 38
datasets of, 38
scholars of, 38–39
Nationalist mobilization, 38
Nationalist movements, 39
Natural language processing technologies (NLP technologies), 15, 19–20, 38–39, 47
New Left movements, 16
New York Times (NYT), 15, 39
News coverage, 188
News media, 190, 209–210
Newspaper
boundaries of true and false positives, 47–49
changing reporting styles of newspapers, 46–47
data, 14–15, 18–19
using newspaper archives for collecting macro-historical data, 40–43
understanding potential limitations and biases of news sources, 40–42
use of digitized news sources, 42–43
Nexis Uni newspaper database, 21–23
North Eastern Women’s Music Retreat (NEWMR), 136–137
planning documents, 137–138
Novel analytics, 7–8
Objectivity in satellite images, algorithms and illusion of, 76–78
Olson method, 77
Online ethnographies, 110
Online platforms, 43
Operationalization, 106–107
Organizational strength, 195
Organizational structure, 240
Organizational type, 25–26
Organizing Together (OT), 181
Outsider-ness in field research, 170–172
Palestinian nationalism, 41
Participant identities, recognizing and negotiating interplay of, 174–175
Participation within movements, determining level of, 175–179
Partisan political context, 195–196
PATRIOT Act (2001), 155
People for the Ethical Treatment of Animals (PETA), 20–21
Periodization, 257–258
Personal transformation, 26
Planet (private commercial satellite company), 75–76
Platform design and evolution, 98–101
Platform/archive, pace or life cycle of, 106
Policing, 232–233
Political mediation models, 192
Political opportunity theory, 230
Political Organization in the News (PONs), 189
Political right, 195
Political sociology, 207–208
Post-event protests, count number of, 222
Pre/post-event protests, count number of, 222
Pro-democracy activists, 243
Processing, innovations in, 4–5
Professional associations, 161–162
Professional organizations, 160–162
sections of, 162
Professional risks, 153
ProQuest (Online platforms), 39, 43, 52
Protest diffusion, 215–216
explanations of, 218–219
forms of action, 227
identifying risk factors of, 226–230
method, 228
results, 228–230
size of protest, 227
social movement organizations, 227–228
studies, 219
variables and operationalization, 227–228
violence, 228
Protest research
beneficence, 148–155
benefits, 148–149
emotional risk, 152–153
ethical treatment of graduate students engaged in, 147–158
individual researchers, 158
journals and academic presses, 162
justice, 157–158
legal risks, 153–155
physical risks, 150–152
practice, 158–163
professional organizations, 160–162
professional risks, 153
respect for persons, 155–157
risks, 149–150
sections of professional organizations, 162
throughout profession, 162–163
Protests, 16, 19–20
for Black Lives, 144
events, 18, 22–23, 26–27, 31, 255
Provenance, 74
concept of, 74
Public education programs, 14
Public health and social science perspectives, 217–218
QAnon, 110–111
Qualitative comparative analysis (QCA), 7, 189, 193–194
data, methods, and measures, 193–196
four hypotheses and QCA results, 196–199
institutional mediation model of social movement news coverage, 189–193
truth tables, Venn diagrams, deviant combinations, and advantages of wide comparisons, 199–207
Qualitative methods course, 177–178
Reagan conservative regime, 200–202
Recognition, 2–3
Regional Public University (RPU), 172–173, 176–177
Repression, 243–244
in instant archive, 101–103
Republican conservative regime, 200–202
Research, 145
participants, 146
process, 122, 148
securing research approval within institutional settings, 179–181
Research design, 225–226
DoCA data, 226
zero-inflated poisson models, 226
Research in Social Movements, Conflict & Change (RSMCC), 1
Research University (RU), 172–173, 176
Researcher, recognizing and negotiating interplay of, 174–175
Researcher bias, 45–47
Resolution, 75
Resource mobilization theory, 188, 233–234
Resources, 240
Risk assessment, 159
Rule-based programming, 249
Rule-based text analysis, 241
Russian military, 69–70
Safety, 144
Satellite imagery, 3
Satellite images, 70, 86
algorithms and illusion of objectivity in, 76–78
case studies and methods, 71–73
incorporating metadata and corroborating satellite images, 81–86
provenance and data quality in satellite images, 73–76
responsible practices for satellite research, 86–88
satellite metadata, 78–81
Satellite sensors, 77
Scale shift, 219–220
Science and technology studies (STS), 73
Search strategy of SSNM dataset, 46
Selection bias, 40–41
Selective repression, 245
Selective threats, 241, 244–245
Severe repression, 244
Shared identity, 240, 242
Shared ideology, 242
Smith College library website, The, 123–124
Social, Political, and Economic Event Database (SPEED), 19–20
Social identities, 169–171, 174
Social media, 3, 93–94, 111–112, 241
companies, 103
data, 94, 108–110
mobilization, 93–94
platforms, 98, 100, 102
Social movement organizations (SMOs), 18–19, 215–216, 227–228
Social movements, 14, 17, 31, 38, 41–42, 144–145, 158, 161–162, 171–172, 188, 227, 240
computational text analysis and potential for reducing bias in collecting data on, 19–20
institutional mediation model of social movement news coverage, 189–193
origin of social movement archives, 122–127
research, 16–20
researchers, 94, 105, 107, 153, 219, 230
scholars, 14–18, 144
South China Morning Post, 251–253
SPEED, 22–23
Standpoint concept, 171
State Actions, 255
State repression, 243–244
State-induced threats, 240
State-led nationalism, 39
State-seeking nationalism, 39
State-Seeking Nationalist Movements (SSNM), 4, 61–62
controlling for increasing number of pages over time, 57–58
data source selection for world-historical analysis, 49–53
dataset I, 43
dataset II, 39
minimizing geographical selection bias, 53–57
using newspaper archives for collecting macro-historical data, 40–43
reliability of SSNM dataset I, 58–64
strategies to reduce false negatives, 45–46
working with keyword strings, 43–49
State-seeking nationalist movements, 39
Strategies, 20
Structural topic modeling (STM), 24–25
Supervised machine-learning methods, 249–250
Syntactic rule-based methods, 8
Syrian civil war, 86–87
Tactics, 20, 24, 27–28
Telegram, 8, 241
comparing events extracted from Telegram and Newspaper sources, 251–253
Telegram Broadcasting Posts, 247–249
Threats, 242–245
Threshold effect, 40–41, 48–49
TikTok, 98–99, 111
Topic models, 24–25
Toronto G20 Summit Protests (2010), 228
Trans-exclusionary radical feminists (TERFs), 132–133
Triangulation and empirical approaches, limitations of single archives and importance of, 110–111
Truth tables and advantages of wide comparisons, 199–207
Twitter, 98
United Kingdom (UK), 39
UK-based newspapers, 56
United States (US), 39
method, 230–231
party affiliation, 230
presidents’ and governors’ party affiliations and diffusion, 230–231
results, 231
US social movements, 193–194, 206
US-based newspapers, 56
variables, 230
Unmanned Aero Systems (UAS), 74
Uppsala Conflict Data Program (UCDP), 73
UCDP/PRIO Armed Conflict Dataset, 221
Venn diagrams and advantages of wide comparisons, 199–207
Veterans, 194–195
Violence, 228
Web scraping, 107–108
Web-crawling techniques, 20
Wen Wei Po, 251–253
White supremacists, 188, 191
Women’s Music Archives, 6, 124–125, 135
case of, 125
World Labor Group database (WLG database), 52
World Wildlife Fund (nonconfrontational EMO), 18–19
YouTube, 111
Yusin Constitution, 243
Zero-Inflated Poisson Models (ZIP models), 226
Earth First! (organization)
, 20–21
EBSCO Regional Business News database
, 21
Emotional “twinges”
, 136–137
Emotional danger
, 149
Emotional exploration
, 137
Emotional risk
, 152–153
Emotions and interactions
, 134–138
Empirical analysis
, 241
Enacted policy
, 195–196
Encyclopedia of Associations
, 21
Enforced policy
, 195–196
Environmental movement
, 20–21, 200
Environmental movement organizations (EMOs)
, 15, 21
Environmental sciences
, 71
Ethical standards for student participation in protest research
Belmont principles
, 146–147
ethics in practice
, 147–158
Ethics
, 71
in practice
, 147–158
Ethnographic research
, 179
Event coalition
, 256–257
measuring
, 254–255
Event coding
, 241
Event diffusion momentum (EDM)
, 7, 216, 220, 233
calculate EDM and confidence intervals
, 222–225
count number of pre/post-event protests
, 222
diffusion studies
, 217–218
explanations of protest diffusion
, 218–219
fit appropriate count models and get predicted counts for each risk factor
, 222
identifying risk factors of protest diffusion
, 226–230
macro-micro analysis of effects of governors’ party affiliations and policing
, 232–233
proposed method for estimating EDM
, 220–225
research design
, 225–226
select event-based data
, 221
set temporal and spatial ranges of interest
, 221–222
toward inter-event approach
, 219–220
US presidents’ and governors’ party affiliations and diffusion
, 230–231
Event history analysis (EHA)
, 219
Event-based data, select
, 221
Events, protests and
, 22–23
Expansionary forms of nationalism
, 39
Facebook
, 98
Faculty research projects
, 144
False negatives, strategies to reduce
, 45–46
Feminist archivists
, 125
Feminist ethnography
boundaries and silences
, 127–134
methods
, 6
origin of social movement archives
, 122–127
voices in archives
, 134–138
Feminist organizations
, 126–127
Feminist research
, 122
Field research
, 170
insider-ness and outsider-ness in
, 170–172
Gale, 52
Gale Cengage Learning, 21, 39, 43
Gay liberation movement, 126
Gentle repression, 244
Geographic information systems (GIS), 78–79
Geographic sciences, 71
Geographical selection bias, 57
checking for effect of repeated mentions, 57
dealing with domestic news, 54–56
minimizing, 53–57
tracing geographical sensitivities, 56
Geographical sensitivities, tracing, 56
Global Data on Events Language and Tone (GDELT), 19–20, 22–23, 38–39, 50
Graduate Assistants, 173
Graduate Student Union Campaign, 173, 177–178, 180–181
Graduate students engaged in protest research, ethical treatment of, 147–158
Graduate training, 144–145
programs, 147
Green State University (GSU), 173
Greenpeace (confrontational EMO), 18–21, 200
Ground truthing
process, 83, 85
techniques, 71
Guardian/The Observer (G/O), 39
High–resolution satellite images, 69–71
Historical connection, 240
Historical newspaper, 43
Historical sources, 59–61
Hong Kong Protests (2019), 228
Indiscriminate repression, 245
Indiscriminate threats, 241, 244–245
Inmediahk. net, 251–253
Innovations, 216
in collection and processing, 4–5
epistemology and reflexivity, 5–7
novel analytics, 7–8
Insider-ness in field research, 170–172
Insider–outsider dynamics and identity in qualitative studies of social movements
case studies, 172–173
graduate student union campaign, 173
insider-ness and outsider-ness in field research, 170–172
Latinx Millennial Student organizing, 172–173
level of participation within movements, 175–179
questions for future researchers, 175, 179, 181
Ragon, 174–175
recognizing and negotiating interplay of researcher and participant identities, 174–175
Reyes, 174–175
securing research approval within institutional settings, 179–181
Instagram, 98–99, 111
Instant archives, 94, 96, 98
archival research and epistemology, 94–96
data and data collection, 105–111
data collection, 107–109
instant archive, 96–98
limitations of single archives and importance of triangulation and empirical approaches, 110–111
methodological decisions, 105–106
pace or life cycle of platform/archive, 106
platform design and evolution, 98–101
repression in, 101–103
research question about individuals, communities, organizations, or discourse, 105–106
sampling and sample size, 109–110
understanding data, 106–107
user innovations, 103–104
Institution-based research on social movements, 171–172
Institutional change, 26, 29
Institutional context, 240
Institutional identity, 173–175
Institutional mediation model of social movement news coverage, 189–193
Institutional repression, 244
Institutional Review Board (IRB), 145, 179, 181
Institutional settings, securing research approval within, 179–181
Institutions, 216
“Insurgent Artifacts” project, 72–73, 83–84
Insurgent groups, 72, 245
Integrated Crisis Early Warning System (ICEWS), 38–39
Inter-event approach, 7, 219–220
Inter-organizational coalitions, 240
“Inter-protest” dynamics, 219–220
Interactions, emotions and, 134–138
International Crisis Early Warning System (ICEWS), 19–20, 22–23
Irredentism, 39
Journals, 162
Justice, 157–158
Karatasli’s analysis, 63–64
Keyword strings, 43, 45
working with, 43–49
Land Remote Sensing Policy Act (1992) (LRSPA), 75
Latinx Millennial Student, 172–173, 176–177, 180
Latinx student organizations, 172–173
League of Women Voters, 240
Legal risks, 153–155
Lesbian feminists, 139
LexisNexis (Online platforms), 39, 43, 52
Liberal Arts College (LAC), 172–173
Log link function, 223–225
Logical remainders, 203–204
Looters’ pits, 78, 81
Looting, 72
LRT–Vuong model selection, 226
Machine algorithms, 73
Machine learning, 19–20, 249
machine learning-based big data projects, 50
machine-learning-based event coding projects, 250
techniques, 15
Machine-learning Protest Event Data System (MPEDS), 22–23
Macro-micro analysis of effects of governors’ party affiliations and policing, 232–233
results, 232–233
variables, 232
Manual data collection, 107–108
Marginalized communities, 124–125, 129–130, 139
Marginalized social movement communities, 122–123
Metadata, 78–79
incorporating metadata and corroborating satellite images, 81–86
Michigan Womyn’s Music Festival, 126
Microsatellites, 75–76
Ming Po, 251–253
Mobilization processes, 220
Music, 17
festivals, 126
National chauvinism, 39
National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, The, 146
National independence wars, 63
National newspapers, 18–19
National Organization for Women (NOW), 126–127, 240
National Research Act (1974), 146
National Science Foundation’s funds for emergent research, 161–162
National US social movement organizations, 208
National Women’s Music Festival, 126
Nationalism, 38
datasets of, 38
scholars of, 38–39
Nationalist mobilization, 38
Nationalist movements, 39
Natural language processing technologies (NLP technologies), 15, 19–20, 38–39, 47
New Left movements, 16
New York Times (NYT), 15, 39
News coverage, 188
News media, 190, 209–210
Newspaper
boundaries of true and false positives, 47–49
changing reporting styles of newspapers, 46–47
data, 14–15, 18–19
using newspaper archives for collecting macro-historical data, 40–43
understanding potential limitations and biases of news sources, 40–42
use of digitized news sources, 42–43
Nexis Uni newspaper database, 21–23
North Eastern Women’s Music Retreat (NEWMR), 136–137
planning documents, 137–138
Novel analytics, 7–8
Objectivity in satellite images, algorithms and illusion of, 76–78
Olson method, 77
Online ethnographies, 110
Online platforms, 43
Operationalization, 106–107
Organizational strength, 195
Organizational structure, 240
Organizational type, 25–26
Organizing Together (OT), 181
Outsider-ness in field research, 170–172
Palestinian nationalism, 41
Participant identities, recognizing and negotiating interplay of, 174–175
Participation within movements, determining level of, 175–179
Partisan political context, 195–196
PATRIOT Act (2001), 155
People for the Ethical Treatment of Animals (PETA), 20–21
Periodization, 257–258
Personal transformation, 26
Planet (private commercial satellite company), 75–76
Platform design and evolution, 98–101
Platform/archive, pace or life cycle of, 106
Policing, 232–233
Political mediation models, 192
Political opportunity theory, 230
Political Organization in the News (PONs), 189
Political right, 195
Political sociology, 207–208
Post-event protests, count number of, 222
Pre/post-event protests, count number of, 222
Pro-democracy activists, 243
Processing, innovations in, 4–5
Professional associations, 161–162
Professional organizations, 160–162
sections of, 162
Professional risks, 153
ProQuest (Online platforms), 39, 43, 52
Protest diffusion, 215–216
explanations of, 218–219
forms of action, 227
identifying risk factors of, 226–230
method, 228
results, 228–230
size of protest, 227
social movement organizations, 227–228
studies, 219
variables and operationalization, 227–228
violence, 228
Protest research
beneficence, 148–155
benefits, 148–149
emotional risk, 152–153
ethical treatment of graduate students engaged in, 147–158
individual researchers, 158
journals and academic presses, 162
justice, 157–158
legal risks, 153–155
physical risks, 150–152
practice, 158–163
professional organizations, 160–162
professional risks, 153
respect for persons, 155–157
risks, 149–150
sections of professional organizations, 162
throughout profession, 162–163
Protests, 16, 19–20
for Black Lives, 144
events, 18, 22–23, 26–27, 31, 255
Provenance, 74
concept of, 74
Public education programs, 14
Public health and social science perspectives, 217–218
QAnon, 110–111
Qualitative comparative analysis (QCA), 7, 189, 193–194
data, methods, and measures, 193–196
four hypotheses and QCA results, 196–199
institutional mediation model of social movement news coverage, 189–193
truth tables, Venn diagrams, deviant combinations, and advantages of wide comparisons, 199–207
Qualitative methods course, 177–178
Reagan conservative regime, 200–202
Recognition, 2–3
Regional Public University (RPU), 172–173, 176–177
Repression, 243–244
in instant archive, 101–103
Republican conservative regime, 200–202
Research, 145
participants, 146
process, 122, 148
securing research approval within institutional settings, 179–181
Research design, 225–226
DoCA data, 226
zero-inflated poisson models, 226
Research in Social Movements, Conflict & Change (RSMCC), 1
Research University (RU), 172–173, 176
Researcher, recognizing and negotiating interplay of, 174–175
Researcher bias, 45–47
Resolution, 75
Resource mobilization theory, 188, 233–234
Resources, 240
Risk assessment, 159
Rule-based programming, 249
Rule-based text analysis, 241
Russian military, 69–70
Safety, 144
Satellite imagery, 3
Satellite images, 70, 86
algorithms and illusion of objectivity in, 76–78
case studies and methods, 71–73
incorporating metadata and corroborating satellite images, 81–86
provenance and data quality in satellite images, 73–76
responsible practices for satellite research, 86–88
satellite metadata, 78–81
Satellite sensors, 77
Scale shift, 219–220
Science and technology studies (STS), 73
Search strategy of SSNM dataset, 46
Selection bias, 40–41
Selective repression, 245
Selective threats, 241, 244–245
Severe repression, 244
Shared identity, 240, 242
Shared ideology, 242
Smith College library website, The, 123–124
Social, Political, and Economic Event Database (SPEED), 19–20
Social identities, 169–171, 174
Social media, 3, 93–94, 111–112, 241
companies, 103
data, 94, 108–110
mobilization, 93–94
platforms, 98, 100, 102
Social movement organizations (SMOs), 18–19, 215–216, 227–228
Social movements, 14, 17, 31, 38, 41–42, 144–145, 158, 161–162, 171–172, 188, 227, 240
computational text analysis and potential for reducing bias in collecting data on, 19–20
institutional mediation model of social movement news coverage, 189–193
origin of social movement archives, 122–127
research, 16–20
researchers, 94, 105, 107, 153, 219, 230
scholars, 14–18, 144
South China Morning Post, 251–253
SPEED, 22–23
Standpoint concept, 171
State Actions, 255
State repression, 243–244
State-induced threats, 240
State-led nationalism, 39
State-seeking nationalism, 39
State-Seeking Nationalist Movements (SSNM), 4, 61–62
controlling for increasing number of pages over time, 57–58
data source selection for world-historical analysis, 49–53
dataset I, 43
dataset II, 39
minimizing geographical selection bias, 53–57
using newspaper archives for collecting macro-historical data, 40–43
reliability of SSNM dataset I, 58–64
strategies to reduce false negatives, 45–46
working with keyword strings, 43–49
State-seeking nationalist movements, 39
Strategies, 20
Structural topic modeling (STM), 24–25
Supervised machine-learning methods, 249–250
Syntactic rule-based methods, 8
Syrian civil war, 86–87
Tactics, 20, 24, 27–28
Telegram, 8, 241
comparing events extracted from Telegram and Newspaper sources, 251–253
Telegram Broadcasting Posts, 247–249
Threats, 242–245
Threshold effect, 40–41, 48–49
TikTok, 98–99, 111
Topic models, 24–25
Toronto G20 Summit Protests (2010), 228
Trans-exclusionary radical feminists (TERFs), 132–133
Triangulation and empirical approaches, limitations of single archives and importance of, 110–111
Truth tables and advantages of wide comparisons, 199–207
Twitter, 98
United Kingdom (UK), 39
UK-based newspapers, 56
United States (US), 39
method, 230–231
party affiliation, 230
presidents’ and governors’ party affiliations and diffusion, 230–231
results, 231
US social movements, 193–194, 206
US-based newspapers, 56
variables, 230
Unmanned Aero Systems (UAS), 74
Uppsala Conflict Data Program (UCDP), 73
UCDP/PRIO Armed Conflict Dataset, 221
Venn diagrams and advantages of wide comparisons, 199–207
Veterans, 194–195
Violence, 228
Web scraping, 107–108
Web-crawling techniques, 20
Wen Wei Po, 251–253
White supremacists, 188, 191
Women’s Music Archives, 6, 124–125, 135
case of, 125
World Labor Group database (WLG database), 52
World Wildlife Fund (nonconfrontational EMO), 18–19
YouTube, 111
Yusin Constitution, 243
Zero-Inflated Poisson Models (ZIP models), 226
Gale
, 52
Gale Cengage Learning
, 21, 39, 43
Gay liberation movement
, 126
Gentle repression
, 244
Geographic information systems (GIS)
, 78–79
Geographic sciences
, 71
Geographical selection bias
, 57
checking for effect of repeated mentions
, 57
dealing with domestic news
, 54–56
minimizing
, 53–57
tracing geographical sensitivities
, 56
Geographical sensitivities, tracing
, 56
Global Data on Events Language and Tone (GDELT)
, 19–20, 22–23, 38–39, 50
Graduate Assistants
, 173
Graduate Student Union Campaign
, 173, 177–178, 180–181
Graduate students engaged in protest research, ethical treatment of
, 147–158
Graduate training
, 144–145
programs
, 147
Green State University (GSU)
, 173
Greenpeace (confrontational EMO)
, 18–21, 200
Ground truthing
process
, 83, 85
techniques
, 71
Guardian/The Observer (G/O)
, 39
High–resolution satellite images
, 69–71
Historical connection
, 240
Historical newspaper
, 43
Historical sources
, 59–61
Hong Kong Protests (2019)
, 228
Indiscriminate repression, 245
Indiscriminate threats, 241, 244–245
Inmediahk. net, 251–253
Innovations, 216
in collection and processing, 4–5
epistemology and reflexivity, 5–7
novel analytics, 7–8
Insider-ness in field research, 170–172
Insider–outsider dynamics and identity in qualitative studies of social movements
case studies, 172–173
graduate student union campaign, 173
insider-ness and outsider-ness in field research, 170–172
Latinx Millennial Student organizing, 172–173
level of participation within movements, 175–179
questions for future researchers, 175, 179, 181
Ragon, 174–175
recognizing and negotiating interplay of researcher and participant identities, 174–175
Reyes, 174–175
securing research approval within institutional settings, 179–181
Instagram, 98–99, 111
Instant archives, 94, 96, 98
archival research and epistemology, 94–96
data and data collection, 105–111
data collection, 107–109
instant archive, 96–98
limitations of single archives and importance of triangulation and empirical approaches, 110–111
methodological decisions, 105–106
pace or life cycle of platform/archive, 106
platform design and evolution, 98–101
repression in, 101–103
research question about individuals, communities, organizations, or discourse, 105–106
sampling and sample size, 109–110
understanding data, 106–107
user innovations, 103–104
Institution-based research on social movements, 171–172
Institutional change, 26, 29
Institutional context, 240
Institutional identity, 173–175
Institutional mediation model of social movement news coverage, 189–193
Institutional repression, 244
Institutional Review Board (IRB), 145, 179, 181
Institutional settings, securing research approval within, 179–181
Institutions, 216
“Insurgent Artifacts” project, 72–73, 83–84
Insurgent groups, 72, 245
Integrated Crisis Early Warning System (ICEWS), 38–39
Inter-event approach, 7, 219–220
Inter-organizational coalitions, 240
“Inter-protest” dynamics, 219–220
Interactions, emotions and, 134–138
International Crisis Early Warning System (ICEWS), 19–20, 22–23
Irredentism, 39
Journals, 162
Justice, 157–158
Karatasli’s analysis, 63–64
Keyword strings, 43, 45
working with, 43–49
Land Remote Sensing Policy Act (1992) (LRSPA), 75
Latinx Millennial Student, 172–173, 176–177, 180
Latinx student organizations, 172–173
League of Women Voters, 240
Legal risks, 153–155
Lesbian feminists, 139
LexisNexis (Online platforms), 39, 43, 52
Liberal Arts College (LAC), 172–173
Log link function, 223–225
Logical remainders, 203–204
Looters’ pits, 78, 81
Looting, 72
LRT–Vuong model selection, 226
Machine algorithms, 73
Machine learning, 19–20, 249
machine learning-based big data projects, 50
machine-learning-based event coding projects, 250
techniques, 15
Machine-learning Protest Event Data System (MPEDS), 22–23
Macro-micro analysis of effects of governors’ party affiliations and policing, 232–233
results, 232–233
variables, 232
Manual data collection, 107–108
Marginalized communities, 124–125, 129–130, 139
Marginalized social movement communities, 122–123
Metadata, 78–79
incorporating metadata and corroborating satellite images, 81–86
Michigan Womyn’s Music Festival, 126
Microsatellites, 75–76
Ming Po, 251–253
Mobilization processes, 220
Music, 17
festivals, 126
National chauvinism, 39
National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, The, 146
National independence wars, 63
National newspapers, 18–19
National Organization for Women (NOW), 126–127, 240
National Research Act (1974), 146
National Science Foundation’s funds for emergent research, 161–162
National US social movement organizations, 208
National Women’s Music Festival, 126
Nationalism, 38
datasets of, 38
scholars of, 38–39
Nationalist mobilization, 38
Nationalist movements, 39
Natural language processing technologies (NLP technologies), 15, 19–20, 38–39, 47
New Left movements, 16
New York Times (NYT), 15, 39
News coverage, 188
News media, 190, 209–210
Newspaper
boundaries of true and false positives, 47–49
changing reporting styles of newspapers, 46–47
data, 14–15, 18–19
using newspaper archives for collecting macro-historical data, 40–43
understanding potential limitations and biases of news sources, 40–42
use of digitized news sources, 42–43
Nexis Uni newspaper database, 21–23
North Eastern Women’s Music Retreat (NEWMR), 136–137
planning documents, 137–138
Novel analytics, 7–8
Objectivity in satellite images, algorithms and illusion of, 76–78
Olson method, 77
Online ethnographies, 110
Online platforms, 43
Operationalization, 106–107
Organizational strength, 195
Organizational structure, 240
Organizational type, 25–26
Organizing Together (OT), 181
Outsider-ness in field research, 170–172
Palestinian nationalism, 41
Participant identities, recognizing and negotiating interplay of, 174–175
Participation within movements, determining level of, 175–179
Partisan political context, 195–196
PATRIOT Act (2001), 155
People for the Ethical Treatment of Animals (PETA), 20–21
Periodization, 257–258
Personal transformation, 26
Planet (private commercial satellite company), 75–76
Platform design and evolution, 98–101
Platform/archive, pace or life cycle of, 106
Policing, 232–233
Political mediation models, 192
Political opportunity theory, 230
Political Organization in the News (PONs), 189
Political right, 195
Political sociology, 207–208
Post-event protests, count number of, 222
Pre/post-event protests, count number of, 222
Pro-democracy activists, 243
Processing, innovations in, 4–5
Professional associations, 161–162
Professional organizations, 160–162
sections of, 162
Professional risks, 153
ProQuest (Online platforms), 39, 43, 52
Protest diffusion, 215–216
explanations of, 218–219
forms of action, 227
identifying risk factors of, 226–230
method, 228
results, 228–230
size of protest, 227
social movement organizations, 227–228
studies, 219
variables and operationalization, 227–228
violence, 228
Protest research
beneficence, 148–155
benefits, 148–149
emotional risk, 152–153
ethical treatment of graduate students engaged in, 147–158
individual researchers, 158
journals and academic presses, 162
justice, 157–158
legal risks, 153–155
physical risks, 150–152
practice, 158–163
professional organizations, 160–162
professional risks, 153
respect for persons, 155–157
risks, 149–150
sections of professional organizations, 162
throughout profession, 162–163
Protests, 16, 19–20
for Black Lives, 144
events, 18, 22–23, 26–27, 31, 255
Provenance, 74
concept of, 74
Public education programs, 14
Public health and social science perspectives, 217–218
QAnon, 110–111
Qualitative comparative analysis (QCA), 7, 189, 193–194
data, methods, and measures, 193–196
four hypotheses and QCA results, 196–199
institutional mediation model of social movement news coverage, 189–193
truth tables, Venn diagrams, deviant combinations, and advantages of wide comparisons, 199–207
Qualitative methods course, 177–178
Reagan conservative regime, 200–202
Recognition, 2–3
Regional Public University (RPU), 172–173, 176–177
Repression, 243–244
in instant archive, 101–103
Republican conservative regime, 200–202
Research, 145
participants, 146
process, 122, 148
securing research approval within institutional settings, 179–181
Research design, 225–226
DoCA data, 226
zero-inflated poisson models, 226
Research in Social Movements, Conflict & Change (RSMCC), 1
Research University (RU), 172–173, 176
Researcher, recognizing and negotiating interplay of, 174–175
Researcher bias, 45–47
Resolution, 75
Resource mobilization theory, 188, 233–234
Resources, 240
Risk assessment, 159
Rule-based programming, 249
Rule-based text analysis, 241
Russian military, 69–70
Safety, 144
Satellite imagery, 3
Satellite images, 70, 86
algorithms and illusion of objectivity in, 76–78
case studies and methods, 71–73
incorporating metadata and corroborating satellite images, 81–86
provenance and data quality in satellite images, 73–76
responsible practices for satellite research, 86–88
satellite metadata, 78–81
Satellite sensors, 77
Scale shift, 219–220
Science and technology studies (STS), 73
Search strategy of SSNM dataset, 46
Selection bias, 40–41
Selective repression, 245
Selective threats, 241, 244–245
Severe repression, 244
Shared identity, 240, 242
Shared ideology, 242
Smith College library website, The, 123–124
Social, Political, and Economic Event Database (SPEED), 19–20
Social identities, 169–171, 174
Social media, 3, 93–94, 111–112, 241
companies, 103
data, 94, 108–110
mobilization, 93–94
platforms, 98, 100, 102
Social movement organizations (SMOs), 18–19, 215–216, 227–228
Social movements, 14, 17, 31, 38, 41–42, 144–145, 158, 161–162, 171–172, 188, 227, 240
computational text analysis and potential for reducing bias in collecting data on, 19–20
institutional mediation model of social movement news coverage, 189–193
origin of social movement archives, 122–127
research, 16–20
researchers, 94, 105, 107, 153, 219, 230
scholars, 14–18, 144
South China Morning Post, 251–253
SPEED, 22–23
Standpoint concept, 171
State Actions, 255
State repression, 243–244
State-induced threats, 240
State-led nationalism, 39
State-seeking nationalism, 39
State-Seeking Nationalist Movements (SSNM), 4, 61–62
controlling for increasing number of pages over time, 57–58
data source selection for world-historical analysis, 49–53
dataset I, 43
dataset II, 39
minimizing geographical selection bias, 53–57
using newspaper archives for collecting macro-historical data, 40–43
reliability of SSNM dataset I, 58–64
strategies to reduce false negatives, 45–46
working with keyword strings, 43–49
State-seeking nationalist movements, 39
Strategies, 20
Structural topic modeling (STM), 24–25
Supervised machine-learning methods, 249–250
Syntactic rule-based methods, 8
Syrian civil war, 86–87
Tactics, 20, 24, 27–28
Telegram, 8, 241
comparing events extracted from Telegram and Newspaper sources, 251–253
Telegram Broadcasting Posts, 247–249
Threats, 242–245
Threshold effect, 40–41, 48–49
TikTok, 98–99, 111
Topic models, 24–25
Toronto G20 Summit Protests (2010), 228
Trans-exclusionary radical feminists (TERFs), 132–133
Triangulation and empirical approaches, limitations of single archives and importance of, 110–111
Truth tables and advantages of wide comparisons, 199–207
Twitter, 98
United Kingdom (UK), 39
UK-based newspapers, 56
United States (US), 39
method, 230–231
party affiliation, 230
presidents’ and governors’ party affiliations and diffusion, 230–231
results, 231
US social movements, 193–194, 206
US-based newspapers, 56
variables, 230
Unmanned Aero Systems (UAS), 74
Uppsala Conflict Data Program (UCDP), 73
UCDP/PRIO Armed Conflict Dataset, 221
Venn diagrams and advantages of wide comparisons, 199–207
Veterans, 194–195
Violence, 228
Web scraping, 107–108
Web-crawling techniques, 20
Wen Wei Po, 251–253
White supremacists, 188, 191
Women’s Music Archives, 6, 124–125, 135
case of, 125
World Labor Group database (WLG database), 52
World Wildlife Fund (nonconfrontational EMO), 18–19
YouTube, 111
Yusin Constitution, 243
Zero-Inflated Poisson Models (ZIP models), 226
Indiscriminate repression
, 245
Indiscriminate threats
, 241, 244–245
Inmediahk. net
, 251–253
Innovations
, 216
in collection and processing
, 4–5
epistemology and reflexivity
, 5–7
novel analytics
, 7–8
Insider-ness in field research
, 170–172
Insider–outsider dynamics and identity in qualitative studies of social movements
case studies
, 172–173
graduate student union campaign
, 173
insider-ness and outsider-ness in field research
, 170–172
Latinx Millennial Student organizing
, 172–173
level of participation within movements
, 175–179
questions for future researchers
, 175, 179, 181
Ragon
, 174–175
recognizing and negotiating interplay of researcher and participant identities
, 174–175
Reyes
, 174–175
securing research approval within institutional settings
, 179–181
Instagram
, 98–99, 111
Instant archives
, 94, 96, 98
archival research and epistemology
, 94–96
data and data collection
, 105–111
data collection
, 107–109
instant archive
, 96–98
limitations of single archives and importance of triangulation and empirical approaches
, 110–111
methodological decisions
, 105–106
pace or life cycle of platform/archive
, 106
platform design and evolution
, 98–101
repression in
, 101–103
research question about individuals, communities, organizations, or discourse
, 105–106
sampling and sample size
, 109–110
understanding data
, 106–107
user innovations
, 103–104
Institution-based research on social movements
, 171–172
Institutional change
, 26, 29
Institutional context
, 240
Institutional identity
, 173–175
Institutional mediation model of social movement news coverage
, 189–193
Institutional repression
, 244
Institutional Review Board (IRB)
, 145, 179, 181
Institutional settings, securing research approval within
, 179–181
Institutions
, 216
“Insurgent Artifacts” project
, 72–73, 83–84
Insurgent groups
, 72, 245
Integrated Crisis Early Warning System (ICEWS)
, 38–39
Inter-event approach
, 7, 219–220
Inter-organizational coalitions
, 240
“Inter-protest” dynamics
, 219–220
Interactions, emotions and
, 134–138
International Crisis Early Warning System (ICEWS)
, 19–20, 22–23
Irredentism
, 39
Journals
, 162
Justice
, 157–158
Karatasli’s analysis, 63–64
Keyword strings, 43, 45
working with, 43–49
Land Remote Sensing Policy Act (1992) (LRSPA), 75
Latinx Millennial Student, 172–173, 176–177, 180
Latinx student organizations, 172–173
League of Women Voters, 240
Legal risks, 153–155
Lesbian feminists, 139
LexisNexis (Online platforms), 39, 43, 52
Liberal Arts College (LAC), 172–173
Log link function, 223–225
Logical remainders, 203–204
Looters’ pits, 78, 81
Looting, 72
LRT–Vuong model selection, 226
Machine algorithms, 73
Machine learning, 19–20, 249
machine learning-based big data projects, 50
machine-learning-based event coding projects, 250
techniques, 15
Machine-learning Protest Event Data System (MPEDS), 22–23
Macro-micro analysis of effects of governors’ party affiliations and policing, 232–233
results, 232–233
variables, 232
Manual data collection, 107–108
Marginalized communities, 124–125, 129–130, 139
Marginalized social movement communities, 122–123
Metadata, 78–79
incorporating metadata and corroborating satellite images, 81–86
Michigan Womyn’s Music Festival, 126
Microsatellites, 75–76
Ming Po, 251–253
Mobilization processes, 220
Music, 17
festivals, 126
National chauvinism, 39
National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, The, 146
National independence wars, 63
National newspapers, 18–19
National Organization for Women (NOW), 126–127, 240
National Research Act (1974), 146
National Science Foundation’s funds for emergent research, 161–162
National US social movement organizations, 208
National Women’s Music Festival, 126
Nationalism, 38
datasets of, 38
scholars of, 38–39
Nationalist mobilization, 38
Nationalist movements, 39
Natural language processing technologies (NLP technologies), 15, 19–20, 38–39, 47
New Left movements, 16
New York Times (NYT), 15, 39
News coverage, 188
News media, 190, 209–210
Newspaper
boundaries of true and false positives, 47–49
changing reporting styles of newspapers, 46–47
data, 14–15, 18–19
using newspaper archives for collecting macro-historical data, 40–43
understanding potential limitations and biases of news sources, 40–42
use of digitized news sources, 42–43
Nexis Uni newspaper database, 21–23
North Eastern Women’s Music Retreat (NEWMR), 136–137
planning documents, 137–138
Novel analytics, 7–8
Objectivity in satellite images, algorithms and illusion of, 76–78
Olson method, 77
Online ethnographies, 110
Online platforms, 43
Operationalization, 106–107
Organizational strength, 195
Organizational structure, 240
Organizational type, 25–26
Organizing Together (OT), 181
Outsider-ness in field research, 170–172
Palestinian nationalism, 41
Participant identities, recognizing and negotiating interplay of, 174–175
Participation within movements, determining level of, 175–179
Partisan political context, 195–196
PATRIOT Act (2001), 155
People for the Ethical Treatment of Animals (PETA), 20–21
Periodization, 257–258
Personal transformation, 26
Planet (private commercial satellite company), 75–76
Platform design and evolution, 98–101
Platform/archive, pace or life cycle of, 106
Policing, 232–233
Political mediation models, 192
Political opportunity theory, 230
Political Organization in the News (PONs), 189
Political right, 195
Political sociology, 207–208
Post-event protests, count number of, 222
Pre/post-event protests, count number of, 222
Pro-democracy activists, 243
Processing, innovations in, 4–5
Professional associations, 161–162
Professional organizations, 160–162
sections of, 162
Professional risks, 153
ProQuest (Online platforms), 39, 43, 52
Protest diffusion, 215–216
explanations of, 218–219
forms of action, 227
identifying risk factors of, 226–230
method, 228
results, 228–230
size of protest, 227
social movement organizations, 227–228
studies, 219
variables and operationalization, 227–228
violence, 228
Protest research
beneficence, 148–155
benefits, 148–149
emotional risk, 152–153
ethical treatment of graduate students engaged in, 147–158
individual researchers, 158
journals and academic presses, 162
justice, 157–158
legal risks, 153–155
physical risks, 150–152
practice, 158–163
professional organizations, 160–162
professional risks, 153
respect for persons, 155–157
risks, 149–150
sections of professional organizations, 162
throughout profession, 162–163
Protests, 16, 19–20
for Black Lives, 144
events, 18, 22–23, 26–27, 31, 255
Provenance, 74
concept of, 74
Public education programs, 14
Public health and social science perspectives, 217–218
QAnon, 110–111
Qualitative comparative analysis (QCA), 7, 189, 193–194
data, methods, and measures, 193–196
four hypotheses and QCA results, 196–199
institutional mediation model of social movement news coverage, 189–193
truth tables, Venn diagrams, deviant combinations, and advantages of wide comparisons, 199–207
Qualitative methods course, 177–178
Reagan conservative regime, 200–202
Recognition, 2–3
Regional Public University (RPU), 172–173, 176–177
Repression, 243–244
in instant archive, 101–103
Republican conservative regime, 200–202
Research, 145
participants, 146
process, 122, 148
securing research approval within institutional settings, 179–181
Research design, 225–226
DoCA data, 226
zero-inflated poisson models, 226
Research in Social Movements, Conflict & Change (RSMCC), 1
Research University (RU), 172–173, 176
Researcher, recognizing and negotiating interplay of, 174–175
Researcher bias, 45–47
Resolution, 75
Resource mobilization theory, 188, 233–234
Resources, 240
Risk assessment, 159
Rule-based programming, 249
Rule-based text analysis, 241
Russian military, 69–70
Safety, 144
Satellite imagery, 3
Satellite images, 70, 86
algorithms and illusion of objectivity in, 76–78
case studies and methods, 71–73
incorporating metadata and corroborating satellite images, 81–86
provenance and data quality in satellite images, 73–76
responsible practices for satellite research, 86–88
satellite metadata, 78–81
Satellite sensors, 77
Scale shift, 219–220
Science and technology studies (STS), 73
Search strategy of SSNM dataset, 46
Selection bias, 40–41
Selective repression, 245
Selective threats, 241, 244–245
Severe repression, 244
Shared identity, 240, 242
Shared ideology, 242
Smith College library website, The, 123–124
Social, Political, and Economic Event Database (SPEED), 19–20
Social identities, 169–171, 174
Social media, 3, 93–94, 111–112, 241
companies, 103
data, 94, 108–110
mobilization, 93–94
platforms, 98, 100, 102
Social movement organizations (SMOs), 18–19, 215–216, 227–228
Social movements, 14, 17, 31, 38, 41–42, 144–145, 158, 161–162, 171–172, 188, 227, 240
computational text analysis and potential for reducing bias in collecting data on, 19–20
institutional mediation model of social movement news coverage, 189–193
origin of social movement archives, 122–127
research, 16–20
researchers, 94, 105, 107, 153, 219, 230
scholars, 14–18, 144
South China Morning Post, 251–253
SPEED, 22–23
Standpoint concept, 171
State Actions, 255
State repression, 243–244
State-induced threats, 240
State-led nationalism, 39
State-seeking nationalism, 39
State-Seeking Nationalist Movements (SSNM), 4, 61–62
controlling for increasing number of pages over time, 57–58
data source selection for world-historical analysis, 49–53
dataset I, 43
dataset II, 39
minimizing geographical selection bias, 53–57
using newspaper archives for collecting macro-historical data, 40–43
reliability of SSNM dataset I, 58–64
strategies to reduce false negatives, 45–46
working with keyword strings, 43–49
State-seeking nationalist movements, 39
Strategies, 20
Structural topic modeling (STM), 24–25
Supervised machine-learning methods, 249–250
Syntactic rule-based methods, 8
Syrian civil war, 86–87
Tactics, 20, 24, 27–28
Telegram, 8, 241
comparing events extracted from Telegram and Newspaper sources, 251–253
Telegram Broadcasting Posts, 247–249
Threats, 242–245
Threshold effect, 40–41, 48–49
TikTok, 98–99, 111
Topic models, 24–25
Toronto G20 Summit Protests (2010), 228
Trans-exclusionary radical feminists (TERFs), 132–133
Triangulation and empirical approaches, limitations of single archives and importance of, 110–111
Truth tables and advantages of wide comparisons, 199–207
Twitter, 98
United Kingdom (UK), 39
UK-based newspapers, 56
United States (US), 39
method, 230–231
party affiliation, 230
presidents’ and governors’ party affiliations and diffusion, 230–231
results, 231
US social movements, 193–194, 206
US-based newspapers, 56
variables, 230
Unmanned Aero Systems (UAS), 74
Uppsala Conflict Data Program (UCDP), 73
UCDP/PRIO Armed Conflict Dataset, 221
Venn diagrams and advantages of wide comparisons, 199–207
Veterans, 194–195
Violence, 228
Web scraping, 107–108
Web-crawling techniques, 20
Wen Wei Po, 251–253
White supremacists, 188, 191
Women’s Music Archives, 6, 124–125, 135
case of, 125
World Labor Group database (WLG database), 52
World Wildlife Fund (nonconfrontational EMO), 18–19
YouTube, 111
Yusin Constitution, 243
Zero-Inflated Poisson Models (ZIP models), 226
Karatasli’s analysis
, 63–64
Keyword strings
, 43, 45
working with
, 43–49
Land Remote Sensing Policy Act (1992) (LRSPA)
, 75
Latinx Millennial Student
, 172–173, 176–177, 180
Latinx student organizations
, 172–173
League of Women Voters
, 240
Legal risks
, 153–155
Lesbian feminists
, 139
LexisNexis (Online platforms)
, 39, 43, 52
Liberal Arts College (LAC)
, 172–173
Log link function
, 223–225
Logical remainders
, 203–204
Looters’ pits
, 78, 81
Looting
, 72
LRT–Vuong model selection
, 226
Machine algorithms, 73
Machine learning, 19–20, 249
machine learning-based big data projects, 50
machine-learning-based event coding projects, 250
techniques, 15
Machine-learning Protest Event Data System (MPEDS), 22–23
Macro-micro analysis of effects of governors’ party affiliations and policing, 232–233
results, 232–233
variables, 232
Manual data collection, 107–108
Marginalized communities, 124–125, 129–130, 139
Marginalized social movement communities, 122–123
Metadata, 78–79
incorporating metadata and corroborating satellite images, 81–86
Michigan Womyn’s Music Festival, 126
Microsatellites, 75–76
Ming Po, 251–253
Mobilization processes, 220
Music, 17
festivals, 126
National chauvinism, 39
National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, The, 146
National independence wars, 63
National newspapers, 18–19
National Organization for Women (NOW), 126–127, 240
National Research Act (1974), 146
National Science Foundation’s funds for emergent research, 161–162
National US social movement organizations, 208
National Women’s Music Festival, 126
Nationalism, 38
datasets of, 38
scholars of, 38–39
Nationalist mobilization, 38
Nationalist movements, 39
Natural language processing technologies (NLP technologies), 15, 19–20, 38–39, 47
New Left movements, 16
New York Times (NYT), 15, 39
News coverage, 188
News media, 190, 209–210
Newspaper
boundaries of true and false positives, 47–49
changing reporting styles of newspapers, 46–47
data, 14–15, 18–19
using newspaper archives for collecting macro-historical data, 40–43
understanding potential limitations and biases of news sources, 40–42
use of digitized news sources, 42–43
Nexis Uni newspaper database, 21–23
North Eastern Women’s Music Retreat (NEWMR), 136–137
planning documents, 137–138
Novel analytics, 7–8
Objectivity in satellite images, algorithms and illusion of, 76–78
Olson method, 77
Online ethnographies, 110
Online platforms, 43
Operationalization, 106–107
Organizational strength, 195
Organizational structure, 240
Organizational type, 25–26
Organizing Together (OT), 181
Outsider-ness in field research, 170–172
Palestinian nationalism, 41
Participant identities, recognizing and negotiating interplay of, 174–175
Participation within movements, determining level of, 175–179
Partisan political context, 195–196
PATRIOT Act (2001), 155
People for the Ethical Treatment of Animals (PETA), 20–21
Periodization, 257–258
Personal transformation, 26
Planet (private commercial satellite company), 75–76
Platform design and evolution, 98–101
Platform/archive, pace or life cycle of, 106
Policing, 232–233
Political mediation models, 192
Political opportunity theory, 230
Political Organization in the News (PONs), 189
Political right, 195
Political sociology, 207–208
Post-event protests, count number of, 222
Pre/post-event protests, count number of, 222
Pro-democracy activists, 243
Processing, innovations in, 4–5
Professional associations, 161–162
Professional organizations, 160–162
sections of, 162
Professional risks, 153
ProQuest (Online platforms), 39, 43, 52
Protest diffusion, 215–216
explanations of, 218–219
forms of action, 227
identifying risk factors of, 226–230
method, 228
results, 228–230
size of protest, 227
social movement organizations, 227–228
studies, 219
variables and operationalization, 227–228
violence, 228
Protest research
beneficence, 148–155
benefits, 148–149
emotional risk, 152–153
ethical treatment of graduate students engaged in, 147–158
individual researchers, 158
journals and academic presses, 162
justice, 157–158
legal risks, 153–155
physical risks, 150–152
practice, 158–163
professional organizations, 160–162
professional risks, 153
respect for persons, 155–157
risks, 149–150
sections of professional organizations, 162
throughout profession, 162–163
Protests, 16, 19–20
for Black Lives, 144
events, 18, 22–23, 26–27, 31, 255
Provenance, 74
concept of, 74
Public education programs, 14
Public health and social science perspectives, 217–218
QAnon, 110–111
Qualitative comparative analysis (QCA), 7, 189, 193–194
data, methods, and measures, 193–196
four hypotheses and QCA results, 196–199
institutional mediation model of social movement news coverage, 189–193
truth tables, Venn diagrams, deviant combinations, and advantages of wide comparisons, 199–207
Qualitative methods course, 177–178
Reagan conservative regime, 200–202
Recognition, 2–3
Regional Public University (RPU), 172–173, 176–177
Repression, 243–244
in instant archive, 101–103
Republican conservative regime, 200–202
Research, 145
participants, 146
process, 122, 148
securing research approval within institutional settings, 179–181
Research design, 225–226
DoCA data, 226
zero-inflated poisson models, 226
Research in Social Movements, Conflict & Change (RSMCC), 1
Research University (RU), 172–173, 176
Researcher, recognizing and negotiating interplay of, 174–175
Researcher bias, 45–47
Resolution, 75
Resource mobilization theory, 188, 233–234
Resources, 240
Risk assessment, 159
Rule-based programming, 249
Rule-based text analysis, 241
Russian military, 69–70
Safety, 144
Satellite imagery, 3
Satellite images, 70, 86
algorithms and illusion of objectivity in, 76–78
case studies and methods, 71–73
incorporating metadata and corroborating satellite images, 81–86
provenance and data quality in satellite images, 73–76
responsible practices for satellite research, 86–88
satellite metadata, 78–81
Satellite sensors, 77
Scale shift, 219–220
Science and technology studies (STS), 73
Search strategy of SSNM dataset, 46
Selection bias, 40–41
Selective repression, 245
Selective threats, 241, 244–245
Severe repression, 244
Shared identity, 240, 242
Shared ideology, 242
Smith College library website, The, 123–124
Social, Political, and Economic Event Database (SPEED), 19–20
Social identities, 169–171, 174
Social media, 3, 93–94, 111–112, 241
companies, 103
data, 94, 108–110
mobilization, 93–94
platforms, 98, 100, 102
Social movement organizations (SMOs), 18–19, 215–216, 227–228
Social movements, 14, 17, 31, 38, 41–42, 144–145, 158, 161–162, 171–172, 188, 227, 240
computational text analysis and potential for reducing bias in collecting data on, 19–20
institutional mediation model of social movement news coverage, 189–193
origin of social movement archives, 122–127
research, 16–20
researchers, 94, 105, 107, 153, 219, 230
scholars, 14–18, 144
South China Morning Post, 251–253
SPEED, 22–23
Standpoint concept, 171
State Actions, 255
State repression, 243–244
State-induced threats, 240
State-led nationalism, 39
State-seeking nationalism, 39
State-Seeking Nationalist Movements (SSNM), 4, 61–62
controlling for increasing number of pages over time, 57–58
data source selection for world-historical analysis, 49–53
dataset I, 43
dataset II, 39
minimizing geographical selection bias, 53–57
using newspaper archives for collecting macro-historical data, 40–43
reliability of SSNM dataset I, 58–64
strategies to reduce false negatives, 45–46
working with keyword strings, 43–49
State-seeking nationalist movements, 39
Strategies, 20
Structural topic modeling (STM), 24–25
Supervised machine-learning methods, 249–250
Syntactic rule-based methods, 8
Syrian civil war, 86–87
Tactics, 20, 24, 27–28
Telegram, 8, 241
comparing events extracted from Telegram and Newspaper sources, 251–253
Telegram Broadcasting Posts, 247–249
Threats, 242–245
Threshold effect, 40–41, 48–49
TikTok, 98–99, 111
Topic models, 24–25
Toronto G20 Summit Protests (2010), 228
Trans-exclusionary radical feminists (TERFs), 132–133
Triangulation and empirical approaches, limitations of single archives and importance of, 110–111
Truth tables and advantages of wide comparisons, 199–207
Twitter, 98
United Kingdom (UK), 39
UK-based newspapers, 56
United States (US), 39
method, 230–231
party affiliation, 230
presidents’ and governors’ party affiliations and diffusion, 230–231
results, 231
US social movements, 193–194, 206
US-based newspapers, 56
variables, 230
Unmanned Aero Systems (UAS), 74
Uppsala Conflict Data Program (UCDP), 73
UCDP/PRIO Armed Conflict Dataset, 221
Venn diagrams and advantages of wide comparisons, 199–207
Veterans, 194–195
Violence, 228
Web scraping, 107–108
Web-crawling techniques, 20
Wen Wei Po, 251–253
White supremacists, 188, 191
Women’s Music Archives, 6, 124–125, 135
case of, 125
World Labor Group database (WLG database), 52
World Wildlife Fund (nonconfrontational EMO), 18–19
YouTube, 111
Yusin Constitution, 243
Zero-Inflated Poisson Models (ZIP models), 226
Machine algorithms
, 73
Machine learning
, 19–20, 249
machine learning-based big data projects
, 50
machine-learning-based event coding projects
, 250
techniques
, 15
Machine-learning Protest Event Data System (MPEDS)
, 22–23
Macro-micro analysis of effects of governors’ party affiliations and policing
, 232–233
results
, 232–233
variables
, 232
Manual data collection
, 107–108
Marginalized communities
, 124–125, 129–130, 139
Marginalized social movement communities
, 122–123
Metadata
, 78–79
incorporating metadata and corroborating satellite images
, 81–86
Michigan Womyn’s Music Festival
, 126
Microsatellites
, 75–76
Ming Po
, 251–253
Mobilization processes
, 220
Music
, 17
festivals
, 126
National chauvinism
, 39
National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, The
, 146
National independence wars
, 63
National newspapers
, 18–19
National Organization for Women (NOW)
, 126–127, 240
National Research Act (1974)
, 146
National Science Foundation’s funds for emergent research
, 161–162
National US social movement organizations
, 208
National Women’s Music Festival
, 126
Nationalism
, 38
datasets of
, 38
scholars of
, 38–39
Nationalist mobilization
, 38
Nationalist movements
, 39
Natural language processing technologies (NLP technologies)
, 15, 19–20, 38–39, 47
New Left movements
, 16
New York Times (NYT)
, 15, 39
News coverage
, 188
News media
, 190, 209–210
Newspaper
boundaries of true and false positives
, 47–49
changing reporting styles of newspapers
, 46–47
data
, 14–15, 18–19
using newspaper archives for collecting macro-historical data
, 40–43
understanding potential limitations and biases of news sources
, 40–42
use of digitized news sources
, 42–43
Nexis Uni newspaper database
, 21–23
North Eastern Women’s Music Retreat (NEWMR)
, 136–137
planning documents
, 137–138
Novel analytics
, 7–8
Objectivity in satellite images, algorithms and illusion of, 76–78
Olson method, 77
Online ethnographies, 110
Online platforms, 43
Operationalization, 106–107
Organizational strength, 195
Organizational structure, 240
Organizational type, 25–26
Organizing Together (OT), 181
Outsider-ness in field research, 170–172
Palestinian nationalism, 41
Participant identities, recognizing and negotiating interplay of, 174–175
Participation within movements, determining level of, 175–179
Partisan political context, 195–196
PATRIOT Act (2001), 155
People for the Ethical Treatment of Animals (PETA), 20–21
Periodization, 257–258
Personal transformation, 26
Planet (private commercial satellite company), 75–76
Platform design and evolution, 98–101
Platform/archive, pace or life cycle of, 106
Policing, 232–233
Political mediation models, 192
Political opportunity theory, 230
Political Organization in the News (PONs), 189
Political right, 195
Political sociology, 207–208
Post-event protests, count number of, 222
Pre/post-event protests, count number of, 222
Pro-democracy activists, 243
Processing, innovations in, 4–5
Professional associations, 161–162
Professional organizations, 160–162
sections of, 162
Professional risks, 153
ProQuest (Online platforms), 39, 43, 52
Protest diffusion, 215–216
explanations of, 218–219
forms of action, 227
identifying risk factors of, 226–230
method, 228
results, 228–230
size of protest, 227
social movement organizations, 227–228
studies, 219
variables and operationalization, 227–228
violence, 228
Protest research
beneficence, 148–155
benefits, 148–149
emotional risk, 152–153
ethical treatment of graduate students engaged in, 147–158
individual researchers, 158
journals and academic presses, 162
justice, 157–158
legal risks, 153–155
physical risks, 150–152
practice, 158–163
professional organizations, 160–162
professional risks, 153
respect for persons, 155–157
risks, 149–150
sections of professional organizations, 162
throughout profession, 162–163
Protests, 16, 19–20
for Black Lives, 144
events, 18, 22–23, 26–27, 31, 255
Provenance, 74
concept of, 74
Public education programs, 14
Public health and social science perspectives, 217–218
QAnon, 110–111
Qualitative comparative analysis (QCA), 7, 189, 193–194
data, methods, and measures, 193–196
four hypotheses and QCA results, 196–199
institutional mediation model of social movement news coverage, 189–193
truth tables, Venn diagrams, deviant combinations, and advantages of wide comparisons, 199–207
Qualitative methods course, 177–178
Reagan conservative regime, 200–202
Recognition, 2–3
Regional Public University (RPU), 172–173, 176–177
Repression, 243–244
in instant archive, 101–103
Republican conservative regime, 200–202
Research, 145
participants, 146
process, 122, 148
securing research approval within institutional settings, 179–181
Research design, 225–226
DoCA data, 226
zero-inflated poisson models, 226
Research in Social Movements, Conflict & Change (RSMCC), 1
Research University (RU), 172–173, 176
Researcher, recognizing and negotiating interplay of, 174–175
Researcher bias, 45–47
Resolution, 75
Resource mobilization theory, 188, 233–234
Resources, 240
Risk assessment, 159
Rule-based programming, 249
Rule-based text analysis, 241
Russian military, 69–70
Safety, 144
Satellite imagery, 3
Satellite images, 70, 86
algorithms and illusion of objectivity in, 76–78
case studies and methods, 71–73
incorporating metadata and corroborating satellite images, 81–86
provenance and data quality in satellite images, 73–76
responsible practices for satellite research, 86–88
satellite metadata, 78–81
Satellite sensors, 77
Scale shift, 219–220
Science and technology studies (STS), 73
Search strategy of SSNM dataset, 46
Selection bias, 40–41
Selective repression, 245
Selective threats, 241, 244–245
Severe repression, 244
Shared identity, 240, 242
Shared ideology, 242
Smith College library website, The, 123–124
Social, Political, and Economic Event Database (SPEED), 19–20
Social identities, 169–171, 174
Social media, 3, 93–94, 111–112, 241
companies, 103
data, 94, 108–110
mobilization, 93–94
platforms, 98, 100, 102
Social movement organizations (SMOs), 18–19, 215–216, 227–228
Social movements, 14, 17, 31, 38, 41–42, 144–145, 158, 161–162, 171–172, 188, 227, 240
computational text analysis and potential for reducing bias in collecting data on, 19–20
institutional mediation model of social movement news coverage, 189–193
origin of social movement archives, 122–127
research, 16–20
researchers, 94, 105, 107, 153, 219, 230
scholars, 14–18, 144
South China Morning Post, 251–253
SPEED, 22–23
Standpoint concept, 171
State Actions, 255
State repression, 243–244
State-induced threats, 240
State-led nationalism, 39
State-seeking nationalism, 39
State-Seeking Nationalist Movements (SSNM), 4, 61–62
controlling for increasing number of pages over time, 57–58
data source selection for world-historical analysis, 49–53
dataset I, 43
dataset II, 39
minimizing geographical selection bias, 53–57
using newspaper archives for collecting macro-historical data, 40–43
reliability of SSNM dataset I, 58–64
strategies to reduce false negatives, 45–46
working with keyword strings, 43–49
State-seeking nationalist movements, 39
Strategies, 20
Structural topic modeling (STM), 24–25
Supervised machine-learning methods, 249–250
Syntactic rule-based methods, 8
Syrian civil war, 86–87
Tactics, 20, 24, 27–28
Telegram, 8, 241
comparing events extracted from Telegram and Newspaper sources, 251–253
Telegram Broadcasting Posts, 247–249
Threats, 242–245
Threshold effect, 40–41, 48–49
TikTok, 98–99, 111
Topic models, 24–25
Toronto G20 Summit Protests (2010), 228
Trans-exclusionary radical feminists (TERFs), 132–133
Triangulation and empirical approaches, limitations of single archives and importance of, 110–111
Truth tables and advantages of wide comparisons, 199–207
Twitter, 98
United Kingdom (UK), 39
UK-based newspapers, 56
United States (US), 39
method, 230–231
party affiliation, 230
presidents’ and governors’ party affiliations and diffusion, 230–231
results, 231
US social movements, 193–194, 206
US-based newspapers, 56
variables, 230
Unmanned Aero Systems (UAS), 74
Uppsala Conflict Data Program (UCDP), 73
UCDP/PRIO Armed Conflict Dataset, 221
Venn diagrams and advantages of wide comparisons, 199–207
Veterans, 194–195
Violence, 228
Web scraping, 107–108
Web-crawling techniques, 20
Wen Wei Po, 251–253
White supremacists, 188, 191
Women’s Music Archives, 6, 124–125, 135
case of, 125
World Labor Group database (WLG database), 52
World Wildlife Fund (nonconfrontational EMO), 18–19
YouTube, 111
Yusin Constitution, 243
Zero-Inflated Poisson Models (ZIP models), 226
Objectivity in satellite images, algorithms and illusion of
, 76–78
Olson method
, 77
Online ethnographies
, 110
Online platforms
, 43
Operationalization
, 106–107
Organizational strength
, 195
Organizational structure
, 240
Organizational type
, 25–26
Organizing Together (OT)
, 181
Outsider-ness in field research
, 170–172
Palestinian nationalism
, 41
Participant identities, recognizing and negotiating interplay of
, 174–175
Participation within movements, determining level of
, 175–179
Partisan political context
, 195–196
PATRIOT Act (2001)
, 155
People for the Ethical Treatment of Animals (PETA)
, 20–21
Periodization
, 257–258
Personal transformation
, 26
Planet (private commercial satellite company)
, 75–76
Platform design and evolution
, 98–101
Platform/archive, pace or life cycle of
, 106
Policing
, 232–233
Political mediation models
, 192
Political opportunity theory
, 230
Political Organization in the News (PONs)
, 189
Political right
, 195
Political sociology
, 207–208
Post-event protests, count number of
, 222
Pre/post-event protests, count number of
, 222
Pro-democracy activists
, 243
Processing, innovations in
, 4–5
Professional associations
, 161–162
Professional organizations
, 160–162
sections of
, 162
Professional risks
, 153
ProQuest (Online platforms)
, 39, 43, 52
Protest diffusion
, 215–216
explanations of
, 218–219
forms of action
, 227
identifying risk factors of
, 226–230
method
, 228
results
, 228–230
size of protest
, 227
social movement organizations
, 227–228
studies
, 219
variables and operationalization
, 227–228
violence
, 228
Protest research
beneficence
, 148–155
benefits
, 148–149
emotional risk
, 152–153
ethical treatment of graduate students engaged in
, 147–158
individual researchers
, 158
journals and academic presses
, 162
justice
, 157–158
legal risks
, 153–155
physical risks
, 150–152
practice
, 158–163
professional organizations
, 160–162
professional risks
, 153
respect for persons
, 155–157
risks
, 149–150
sections of professional organizations
, 162
throughout profession
, 162–163
Protests
, 16, 19–20
for Black Lives
, 144
events
, 18, 22–23, 26–27, 31, 255
Provenance
, 74
concept of
, 74
Public education programs
, 14
Public health and social science perspectives
, 217–218
QAnon, 110–111
Qualitative comparative analysis (QCA), 7, 189, 193–194
data, methods, and measures, 193–196
four hypotheses and QCA results, 196–199
institutional mediation model of social movement news coverage, 189–193
truth tables, Venn diagrams, deviant combinations, and advantages of wide comparisons, 199–207
Qualitative methods course, 177–178
Reagan conservative regime, 200–202
Recognition, 2–3
Regional Public University (RPU), 172–173, 176–177
Repression, 243–244
in instant archive, 101–103
Republican conservative regime, 200–202
Research, 145
participants, 146
process, 122, 148
securing research approval within institutional settings, 179–181
Research design, 225–226
DoCA data, 226
zero-inflated poisson models, 226
Research in Social Movements, Conflict & Change (RSMCC), 1
Research University (RU), 172–173, 176
Researcher, recognizing and negotiating interplay of, 174–175
Researcher bias, 45–47
Resolution, 75
Resource mobilization theory, 188, 233–234
Resources, 240
Risk assessment, 159
Rule-based programming, 249
Rule-based text analysis, 241
Russian military, 69–70
Safety, 144
Satellite imagery, 3
Satellite images, 70, 86
algorithms and illusion of objectivity in, 76–78
case studies and methods, 71–73
incorporating metadata and corroborating satellite images, 81–86
provenance and data quality in satellite images, 73–76
responsible practices for satellite research, 86–88
satellite metadata, 78–81
Satellite sensors, 77
Scale shift, 219–220
Science and technology studies (STS), 73
Search strategy of SSNM dataset, 46
Selection bias, 40–41
Selective repression, 245
Selective threats, 241, 244–245
Severe repression, 244
Shared identity, 240, 242
Shared ideology, 242
Smith College library website, The, 123–124
Social, Political, and Economic Event Database (SPEED), 19–20
Social identities, 169–171, 174
Social media, 3, 93–94, 111–112, 241
companies, 103
data, 94, 108–110
mobilization, 93–94
platforms, 98, 100, 102
Social movement organizations (SMOs), 18–19, 215–216, 227–228
Social movements, 14, 17, 31, 38, 41–42, 144–145, 158, 161–162, 171–172, 188, 227, 240
computational text analysis and potential for reducing bias in collecting data on, 19–20
institutional mediation model of social movement news coverage, 189–193
origin of social movement archives, 122–127
research, 16–20
researchers, 94, 105, 107, 153, 219, 230
scholars, 14–18, 144
South China Morning Post, 251–253
SPEED, 22–23
Standpoint concept, 171
State Actions, 255
State repression, 243–244
State-induced threats, 240
State-led nationalism, 39
State-seeking nationalism, 39
State-Seeking Nationalist Movements (SSNM), 4, 61–62
controlling for increasing number of pages over time, 57–58
data source selection for world-historical analysis, 49–53
dataset I, 43
dataset II, 39
minimizing geographical selection bias, 53–57
using newspaper archives for collecting macro-historical data, 40–43
reliability of SSNM dataset I, 58–64
strategies to reduce false negatives, 45–46
working with keyword strings, 43–49
State-seeking nationalist movements, 39
Strategies, 20
Structural topic modeling (STM), 24–25
Supervised machine-learning methods, 249–250
Syntactic rule-based methods, 8
Syrian civil war, 86–87
Tactics, 20, 24, 27–28
Telegram, 8, 241
comparing events extracted from Telegram and Newspaper sources, 251–253
Telegram Broadcasting Posts, 247–249
Threats, 242–245
Threshold effect, 40–41, 48–49
TikTok, 98–99, 111
Topic models, 24–25
Toronto G20 Summit Protests (2010), 228
Trans-exclusionary radical feminists (TERFs), 132–133
Triangulation and empirical approaches, limitations of single archives and importance of, 110–111
Truth tables and advantages of wide comparisons, 199–207
Twitter, 98
United Kingdom (UK), 39
UK-based newspapers, 56
United States (US), 39
method, 230–231
party affiliation, 230
presidents’ and governors’ party affiliations and diffusion, 230–231
results, 231
US social movements, 193–194, 206
US-based newspapers, 56
variables, 230
Unmanned Aero Systems (UAS), 74
Uppsala Conflict Data Program (UCDP), 73
UCDP/PRIO Armed Conflict Dataset, 221
Venn diagrams and advantages of wide comparisons, 199–207
Veterans, 194–195
Violence, 228
Web scraping, 107–108
Web-crawling techniques, 20
Wen Wei Po, 251–253
White supremacists, 188, 191
Women’s Music Archives, 6, 124–125, 135
case of, 125
World Labor Group database (WLG database), 52
World Wildlife Fund (nonconfrontational EMO), 18–19
YouTube, 111
Yusin Constitution, 243
Zero-Inflated Poisson Models (ZIP models), 226
QAnon
, 110–111
Qualitative comparative analysis (QCA)
, 7, 189, 193–194
data, methods, and measures
, 193–196
four hypotheses and QCA results
, 196–199
institutional mediation model of social movement news coverage
, 189–193
truth tables, Venn diagrams, deviant combinations, and advantages of wide comparisons
, 199–207
Qualitative methods course
, 177–178
Reagan conservative regime
, 200–202
Recognition
, 2–3
Regional Public University (RPU)
, 172–173, 176–177
Repression
, 243–244
in instant archive
, 101–103
Republican conservative regime
, 200–202
Research
, 145
participants
, 146
process
, 122, 148
securing research approval within institutional settings
, 179–181
Research design
, 225–226
DoCA data
, 226
zero-inflated poisson models
, 226
Research in Social Movements, Conflict & Change (RSMCC)
, 1
Research University (RU)
, 172–173, 176
Researcher, recognizing and negotiating interplay of
, 174–175
Researcher bias
, 45–47
Resolution
, 75
Resource mobilization theory
, 188, 233–234
Resources
, 240
Risk assessment
, 159
Rule-based programming
, 249
Rule-based text analysis
, 241
Russian military
, 69–70
Safety, 144
Satellite imagery, 3
Satellite images, 70, 86
algorithms and illusion of objectivity in, 76–78
case studies and methods, 71–73
incorporating metadata and corroborating satellite images, 81–86
provenance and data quality in satellite images, 73–76
responsible practices for satellite research, 86–88
satellite metadata, 78–81
Satellite sensors, 77
Scale shift, 219–220
Science and technology studies (STS), 73
Search strategy of SSNM dataset, 46
Selection bias, 40–41
Selective repression, 245
Selective threats, 241, 244–245
Severe repression, 244
Shared identity, 240, 242
Shared ideology, 242
Smith College library website, The, 123–124
Social, Political, and Economic Event Database (SPEED), 19–20
Social identities, 169–171, 174
Social media, 3, 93–94, 111–112, 241
companies, 103
data, 94, 108–110
mobilization, 93–94
platforms, 98, 100, 102
Social movement organizations (SMOs), 18–19, 215–216, 227–228
Social movements, 14, 17, 31, 38, 41–42, 144–145, 158, 161–162, 171–172, 188, 227, 240
computational text analysis and potential for reducing bias in collecting data on, 19–20
institutional mediation model of social movement news coverage, 189–193
origin of social movement archives, 122–127
research, 16–20
researchers, 94, 105, 107, 153, 219, 230
scholars, 14–18, 144
South China Morning Post, 251–253
SPEED, 22–23
Standpoint concept, 171
State Actions, 255
State repression, 243–244
State-induced threats, 240
State-led nationalism, 39
State-seeking nationalism, 39
State-Seeking Nationalist Movements (SSNM), 4, 61–62
controlling for increasing number of pages over time, 57–58
data source selection for world-historical analysis, 49–53
dataset I, 43
dataset II, 39
minimizing geographical selection bias, 53–57
using newspaper archives for collecting macro-historical data, 40–43
reliability of SSNM dataset I, 58–64
strategies to reduce false negatives, 45–46
working with keyword strings, 43–49
State-seeking nationalist movements, 39
Strategies, 20
Structural topic modeling (STM), 24–25
Supervised machine-learning methods, 249–250
Syntactic rule-based methods, 8
Syrian civil war, 86–87
Tactics, 20, 24, 27–28
Telegram, 8, 241
comparing events extracted from Telegram and Newspaper sources, 251–253
Telegram Broadcasting Posts, 247–249
Threats, 242–245
Threshold effect, 40–41, 48–49
TikTok, 98–99, 111
Topic models, 24–25
Toronto G20 Summit Protests (2010), 228
Trans-exclusionary radical feminists (TERFs), 132–133
Triangulation and empirical approaches, limitations of single archives and importance of, 110–111
Truth tables and advantages of wide comparisons, 199–207
Twitter, 98
United Kingdom (UK), 39
UK-based newspapers, 56
United States (US), 39
method, 230–231
party affiliation, 230
presidents’ and governors’ party affiliations and diffusion, 230–231
results, 231
US social movements, 193–194, 206
US-based newspapers, 56
variables, 230
Unmanned Aero Systems (UAS), 74
Uppsala Conflict Data Program (UCDP), 73
UCDP/PRIO Armed Conflict Dataset, 221
Venn diagrams and advantages of wide comparisons, 199–207
Veterans, 194–195
Violence, 228
Web scraping, 107–108
Web-crawling techniques, 20
Wen Wei Po, 251–253
White supremacists, 188, 191
Women’s Music Archives, 6, 124–125, 135
case of, 125
World Labor Group database (WLG database), 52
World Wildlife Fund (nonconfrontational EMO), 18–19
YouTube, 111
Yusin Constitution, 243
Zero-Inflated Poisson Models (ZIP models), 226
Safety
, 144
Satellite imagery
, 3
Satellite images
, 70, 86
algorithms and illusion of objectivity in
, 76–78
case studies and methods
, 71–73
incorporating metadata and corroborating satellite images
, 81–86
provenance and data quality in satellite images
, 73–76
responsible practices for satellite research
, 86–88
satellite metadata
, 78–81
Satellite sensors
, 77
Scale shift
, 219–220
Science and technology studies (STS)
, 73
Search strategy of SSNM dataset
, 46
Selection bias
, 40–41
Selective repression
, 245
Selective threats
, 241, 244–245
Severe repression
, 244
Shared identity
, 240, 242
Shared ideology
, 242
Smith College library website, The
, 123–124
Social, Political, and Economic Event Database (SPEED)
, 19–20
Social identities
, 169–171, 174
Social media
, 3, 93–94, 111–112, 241
companies
, 103
data
, 94, 108–110
mobilization
, 93–94
platforms
, 98, 100, 102
Social movement organizations (SMOs)
, 18–19, 215–216, 227–228
Social movements
, 14, 17, 31, 38, 41–42, 144–145, 158, 161–162, 171–172, 188, 227, 240
computational text analysis and potential for reducing bias in collecting data on
, 19–20
institutional mediation model of social movement news coverage
, 189–193
origin of social movement archives
, 122–127
research
, 16–20
researchers
, 94, 105, 107, 153, 219, 230
scholars
, 14–18, 144
South China Morning Post
, 251–253
SPEED
, 22–23
Standpoint concept
, 171
State Actions
, 255
State repression
, 243–244
State-induced threats
, 240
State-led nationalism
, 39
State-seeking nationalism
, 39
State-Seeking Nationalist Movements (SSNM)
, 4, 61–62
controlling for increasing number of pages over time
, 57–58
data source selection for world-historical analysis
, 49–53
dataset I
, 43
dataset II
, 39
minimizing geographical selection bias
, 53–57
using newspaper archives for collecting macro-historical data
, 40–43
reliability of SSNM dataset I
, 58–64
strategies to reduce false negatives
, 45–46
working with keyword strings
, 43–49
State-seeking nationalist movements
, 39
Strategies
, 20
Structural topic modeling (STM)
, 24–25
Supervised machine-learning methods
, 249–250
Syntactic rule-based methods
, 8
Syrian civil war
, 86–87
Tactics
, 20, 24, 27–28
Telegram
, 8, 241
comparing events extracted from Telegram and Newspaper sources
, 251–253
Telegram Broadcasting Posts
, 247–249
Threats
, 242–245
Threshold effect
, 40–41, 48–49
TikTok
, 98–99, 111
Topic models
, 24–25
Toronto G20 Summit Protests (2010)
, 228
Trans-exclusionary radical feminists (TERFs)
, 132–133
Triangulation and empirical approaches, limitations of single archives and importance of
, 110–111
Truth tables and advantages of wide comparisons
, 199–207
Twitter
, 98
United Kingdom (UK), 39
UK-based newspapers, 56
United States (US), 39
method, 230–231
party affiliation, 230
presidents’ and governors’ party affiliations and diffusion, 230–231
results, 231
US social movements, 193–194, 206
US-based newspapers, 56
variables, 230
Unmanned Aero Systems (UAS), 74
Uppsala Conflict Data Program (UCDP), 73
UCDP/PRIO Armed Conflict Dataset, 221
Venn diagrams and advantages of wide comparisons, 199–207
Veterans, 194–195
Violence, 228
Web scraping, 107–108
Web-crawling techniques, 20
Wen Wei Po, 251–253
White supremacists, 188, 191
Women’s Music Archives, 6, 124–125, 135
case of, 125
World Labor Group database (WLG database), 52
World Wildlife Fund (nonconfrontational EMO), 18–19
YouTube, 111
Yusin Constitution, 243
Zero-Inflated Poisson Models (ZIP models), 226
United Kingdom (UK)
, 39
UK-based newspapers
, 56
United States (US)
, 39
method
, 230–231
party affiliation
, 230
presidents’ and governors’ party affiliations and diffusion
, 230–231
results
, 231
US social movements
, 193–194, 206
US-based newspapers
, 56
variables
, 230
Unmanned Aero Systems (UAS)
, 74
Uppsala Conflict Data Program (UCDP)
, 73
UCDP/PRIO Armed Conflict Dataset
, 221
Venn diagrams and advantages of wide comparisons
, 199–207
Veterans
, 194–195
Violence
, 228
Web scraping, 107–108
Web-crawling techniques, 20
Wen Wei Po, 251–253
White supremacists, 188, 191
Women’s Music Archives, 6, 124–125, 135
case of, 125
World Labor Group database (WLG database), 52
World Wildlife Fund (nonconfrontational EMO), 18–19
YouTube, 111
Yusin Constitution, 243
Zero-Inflated Poisson Models (ZIP models), 226
Web scraping
, 107–108
Web-crawling techniques
, 20
Wen Wei Po
, 251–253
White supremacists
, 188, 191
Women’s Music Archives
, 6, 124–125, 135
case of
, 125
World Labor Group database (WLG database)
, 52
World Wildlife Fund (nonconfrontational EMO)
, 18–19
YouTube
, 111
Yusin Constitution
, 243
Zero-Inflated Poisson Models (ZIP models), 226
Zero-Inflated Poisson Models (ZIP models)
, 226
- Prelims
- Navigating Interests and Cultivating Innovation in the Study of Social Movements, Conflict, and Change
- Section I Innovations in Data Collection and Processing
- Beyond Protests: Using Computational Text Analysis to Explore a Greater Variety of Social Movement Activities
- The Use of Digitized Newspaper Archives for World-Historical Research on Social Conflicts: The State-Seeking Nationalist Movements Database
- Satellite Images in Conflict Research: Methodological and Ethical Considerations
- Instant Archives: Social Media and Social Movement Research
- Section II Epistemology and Reflexivity
- Moments of Interrogation: Doing Feminist Ethnography in the Archives
- Developing Ethical Standards for Student Participation in Protest Research
- Insider-Outsider Dynamics and Identity in Qualitative Studies of Social Movements
- Section III Novel Analytics
- How to Analyze the Influence of Social Movements With QCA: Combinational Hypotheses, Venn Diagrams, and Movements Making Big News
- Measuring Event Diffusion Momentum (EDM): Applications in Social Movement Research
- Coalitions Under Threat: Analyzing the 2019 Hong Kong Anti-Extradition Protests Using Telegram Social Media Data
- Index